Business

Research area: My research focuses on the development and application of advanced quantitative and analytical methods to support decision-making in complex systems. Specifically, I work in the areas of optimization, decision theory, multi-criteria decision making (MCDM), inventory modelling, time series analysis, fuzzy set theory, machine learning, and supply chain analytics. My research aims to develop innovative models and decision-support frameworks that help organizations improve efficiency, resilience, sustainability, and operational performance under uncertainty. By integrating operations research, artificial intelligence, forecasting techniques, and uncertainty modelling, I seek to address real-world challenges in supply chain management, logistics, healthcare operations, and business decision-making while bridging the

Student roles:
The student will serve as a key member of the research team and will be actively involved in all phases of the research process, from problem identification to the dissemination of results. Responsibilities will include conducting comprehensive literature reviews to identify research gaps and emerging trends, collecting, cleaning, and analyzing quantitative and qualitative data, and developing mathematical, statistical, and computational models to address complex decision-making problems. The student will assist in designing and implementing optimization models, machine learning algorithms, forecasting techniques, inventory management models, and multi-criteria decision-making frameworks under both deterministic and uncertain environments. The role will also involve performing computational experiments, sensitivity analyses, scenario evaluations, and model validation using real-world datasets from supply chain, logistics, healthcare, sustainability, and business applications.

In addition, the student will contribute to the development of innovative methodologies by integrating techniques from operations research, artificial intelligence, fuzzy set theory, and data analytics. The student will be expected to interpret research findings, generate managerial insights, and translate technical results into practical recommendations for decision-makers. Responsibilities will also include preparing research reports, assisting with grant-funded projects, presenting findings at research group meetings, conferences, and workshops, and contributing to the preparation of manuscripts for publication in high-quality academic journals. The successful candidate should demonstrate strong analytical and problem-solving abilities, intellectual curiosity, initiative, and the capacity to work both independently and collaboratively within an interdisciplinary research environment. Through this role, the student will gain valuable experience in advanced analytical methods, academic research, scientific writing, and industry-focused problem solving while contributing to impactful research in supply chain management, logistics, healthcare operations, sustainability, and business decision-making.

Skills required:
Students should have a background in quantitative methods, mathematics, statistics, operations research, industrial engineering, SCM, business analytics or a related discipline. Experience in OR, decision analysis, data analytics, machine learning or MCDM methods would be highly beneficial. The student should possess strong analytical and problem-solving skills, be comfortable working with mathematical models and large datasets, and have programming experience. Knowledge of statistical analysis and artificial intelligence is an asset. The successful candidate should also demonstrate strong communication skills, independence, and a willingness to engage in interdisciplinary research that addresses practical challenges in SCM, logistics, healthcare systems, and business operations.

Research area: This project focuses on artificial intelligence governance, risk management, and organizational analytics applied to mid-sized organizations that are adopting AI systems under resource constraints. It develops practical ways to measure governance maturity—covering accountability, policy clarity, human oversight, transparency, and incident readiness—so leaders can prioritize improvements without duplicating enterprise-grade compliance frameworks. The work integrates organizational behavior, information systems, and responsible-AI perspectives to produce scorecards that are interpretable for executives and operational teams alike, with explicit attention to proportionality and feasibility.

Student roles:
The intern will support the design of the governance framework and scorecard: refining indicator definitions, sourcing publicly available guidance and standards for alignment, and preparing coding schemes for document review. The intern will assist with structured data collection (e.g., policy inventories, survey deployment logistics, anonymized interview or survey notes), perform reliability checks, and help analyze patterns that differentiate maturity levels across dimensions.

The intern will contribute to visualization and reporting—tables, radar or bar summaries, and short narratives that explain what scores mean for decision-makers. Throughout, the intern will maintain careful records, follow ethical data handling practices, and revise materials based on supervisor feedback. Final deliverables include polished documentation suitable for both academic and practitioner audiences, with explicit discussion of scope limits, assumptions, and next steps for piloting the scorecard in partner organizations.

Skills required:
Ideal candidates have a strong interest in AI policy, responsible innovation, and organizational analytics, and are comfortable with qualitative and mixed-methods research. Coursework or experience in management information systems, public policy, ethics, or risk management is helpful. Basic quantitative literacy (descriptive statistics, simple weighting or aggregation) and strong writing skills are important for defining indicators and communicating results clearly. Familiarity with spreadsheets or light scripting for scoring prototypes is useful but not mandatory.

Research area: My research interests are in applied analytics & machine learning, business process improvement, and optimization. My current and past research focuses on cross-disciplinary approaches to address business improvement and optimization problems. For instance, the application of machine learning, evolutionary algorithms, combinatorial optimization, simulation modelling, Lean/Six Sigma, and autonomous decision-making to industrial/service process improvement, with a focus on process, people, and technology. Business Intelligence Data Analytics Data Visualization Machine Learning Text Mining - NLP Transformer-based models/LLMs for qualitative analysis Python Programming

Student roles:
The student research assistant will be responsible for:
- reviewing literature on supply chain resilience, disruption forecasting, risk profiling, sustainable supply chains, and AI-enabled decision support;
- helping identify relevant structured and unstructured data sources related to supply chain disruptions, supplier risk, logistics performance, climate events, trade risks, and sustainability indicators;
- cleaning, organizing, and harmonizing datasets for use in AI/ML-based risk profiling;
- developing relevant features such as disruption frequency, supplier performance indicators, delivery reliability, risk exposure, sector vulnerability, and sustainability-related indicators;
- testing machine learning methods such as classification, clustering, anomaly detection, or risk scoring to identify vulnerable suppliers, routes, sectors, or supply chain nodes;
- comparing model outputs and interpreting risk patterns in terms of resilience and sustainability implications;
- developing visual outputs such as risk maps, heat maps, dashboards, classification summaries, and scenario-oriented charts;
- documenting data sources, preprocessing steps, modelling assumptions, evaluation results, and limitations;
- assisting in the development of a proof-of-concept risk profiling dashboard that can serve as an input for future supply chain digital twin development.

Skills required:
The student should have an interest in supply chain analytics, machine learning, sustainability, and data-driven decision support. Skills in Python, Pandas, Scikit-learn, data cleaning, feature engineering, visualization, and basic machine learning are desirable. Familiarity with time-series analysis, classification models, clustering, risk scoring, APIs, SQL, Power BI, Tableau, or simulation tools would be an asset. The student should be able to conduct literature reviews, organize datasets, test analytical models, interpret results, and communicate findings clearly.

Research area: My research interests are in applied analytics & machine learning, business process improvement, and optimization. My current and past research focuses on cross-disciplinary approaches to address business improvement and optimization problems. For instance, the application of machine learning, evolutionary algorithms, combinatorial optimization, simulation modelling, Lean/Six Sigma, and autonomous decision-making to industrial/service process improvement, with a focus on process, people, and technology. Business Intelligence Data Analytics Data Visualization Machine Learning Text Mining - NLP Transformer-based models/LLMs for qualitative analysis Python Programming

Student roles:
The student research assistant will be responsible for:
- reviewing literature on AI-supported learning, discussion-based pedagogy, formative feedback, learning analytics, and educational simulations;
- helping define the functional requirements for an AI-enhanced discussion learning platform;
- designing a prototype system architecture for instructor, student, and analytics modules;
- developing a basic web-based platform where instructors can create prompts, learning objectives, and rubric criteria;
- developing a student-facing interface for drafting discussion posts and receiving AI-generated formative feedback;
- designing prompt templates and evaluation logic for feedback on clarity, relevance, critical thinking, evidence use, course concept connection, and reflection depth;
- developing a simulation mechanism that generates AI-based peer, stakeholder, or counterargument responses to student posts;
- implementing basic NLP or LLM-based analytics to classify discussion posts by theme, concept coverage, quality indicators, and possible misconceptions;
- developing an instructor dashboard to summarize discussion patterns, missing concepts, common themes, and student engagement indicators;
- testing the prototype using sample, synthetic, or anonymized discussion posts;
- documenting system design, model/prompt logic, testing procedures, limitations, ethical considerations, and potential future improvements.

Skills required:
The student should have an interest in AI, machine learning, educational technology, and web-based system development. Technical skills in Python, JavaScript, HTML/CSS, web application development, APIs, databases, LLM prompting, NLP, and data visualization are desirable. Experience with frameworks such as Flask, Django, FastAPI, React, Streamlit, or similar tools would be an asset. The student should be able to conduct literature reviews, prototype software, test AI-generated outputs, analyze text data, document system design, and communicate findings clearly.

Research area: My research focuses on project management and decision-making in complex product development, particularly in aviation and advanced mobility contexts. I study how artificial intelligence, systems engineering, and project governance can support managers and engineering teams when projects involve high uncertainty, technical interdependencies, evolving requirements, and strategic risks.

Student roles:
The intern will contribute to one of two complementary roles. The first role focuses on conducting a structured literature review on agentic AI, decision support systems, project management, systems engineering, and complex aviation product development. The second role focuses on advancing a prototype of an AI-supported project management assistant by designing AI-agent workflows, organizing project data, testing decision-support scenarios, and documenting technical choices. Both interns will participate in research meetings, produce written outputs, and contribute to the development of a conceptual and practical framework for AI-supported decision-making.

Skills required:
Two complementary student profiles are sought. For the literature review position, the student should have an interest in academic research, critical analysis, and scientific writing, with a background in project management, engineering, management, technology studies, or a related field. Experience conducting literature reviews and synthesizing research findings is an asset.
For the technical development position, the student should have a background in computer science, artificial intelligence, data science, software engineering, or a related discipline. Experience with Python, AI tools, data processing, prototyping, and software development is desirable.

Research area: This project examines multi-agent artificial intelligence systems that coordinate specialized components—such as planning, retrieval, memory, and tool use—to automate structured business processes. It combines ideas from intelligent automation, software engineering, and human–computer interaction to understand when agentic workflows are reliable, auditable, and cost-effective compared with conventional scripting. The research emphasizes prototype development, empirical evaluation, and responsible design practices for deploying large language model–based agents in realistic operational settings, including clear logging, evaluation metrics, and safeguards against misuse. The objective is practical guidance organizations can use to adopt automation without sacrificing transparency.

Student roles:
The intern will translate business-style tasks into concrete workflow specifications, then implement multi-agent prototypes using LLMs and tool integrations selected with the supervisor. Early work focuses on environment setup, baseline agents, and reproducible evaluation harnesses; the intern will maintain a structured log of runs, costs, errors, and configuration changes to support rigorous comparison across iterations.

Mid-project, the intern will iterate on agent roles, prompts, and orchestration logic; run structured tests across tasks such as market analysis and data collection; and quantify performance trade-offs including accuracy, runtime, and API spend. The intern will also build visualizations or simple dashboards as needed, capture representative failure cases, and propose mitigations such as verification steps, human-in-the-loop checkpoints, constrained tool access, or output schemas that reduce ambiguity.

In the final phase, the intern will consolidate findings into documented case studies, prepare a results summary with clear limitations, and support preparation of figures and tables for reporting. The intern is expected to meet regularly with the supervisor, manage scope proactively, follow responsible use guidelines for third-party APIs, and treat any illustrative proprietary details as confidential unless explicitly approved for disclosure. The intern will also archive code and configuration snapshots so results can be independently verified.

Skills required:
Candidates should be proficient in Python and comfortable working with APIs, virtual environments, and version control. Familiarity with large language models, prompt engineering, and common integration patterns (REST calls, JSON handling, basic authentication) is important. A basic understanding of business process modeling or workflow automation concepts is helpful, as is prior exposure to evaluation practices such as test cases, logging, and simple benchmarking. The student should be organized, security-conscious when handling keys and data, and able to document experiments clearly for reproducibility.

Research area: My research focuses on digital transformation, digital platforms, and organizational adoption of emerging technologies. I study how organizations implement, govern, and use AI-enabled systems, with attention to platform ecosystems, boundary resources, human–AI interaction, trust, and change management. In a current research project on AI-enabled digital twins for building decarbonization, my work examines how digital twin capabilities can be accessed, extended, and governed across organizational and technical boundaries.

Student roles:
The students will support a focused 12-week exploratory study on boundary resources for AI-enabled smart building digital twins. Their role will be to identify, categorize, and synthesize examples of boundary resources that enable different actors to access, use, or extend digital twin capabilities. The students will first conduct a targeted literature review on digital twins, smart buildings, digital platforms, and boundary resources. They will help identify relevant boundary resources such as APIs, dashboards, data-sharing mechanisms, semantic standards, documentation, modular tools, and governance rules. They will then support the development of a simple typology that explains what these boundary resources are, who uses them, and how they support interoperability, controlled openness, value co-creation, and governance. The students may also help develop a stakeholder map of key actors in AI-enabled smart building digital twin ecosystems, such as building operators, facility managers, engineers, technology providers, researchers, and public-sector organizations. Expected outputs include an annotated bibliography, literature summary, boundary resource typology, stakeholder map, and short final research report. If two students are hired, one may focus on literature synthesis and typology development, while the other may focus on stakeholder mapping and examples from smart building and decarbonization contexts.

Skills required:
The student should have a background in information systems, management, engineering management, computer science, human–computer interaction, or a related field. Interest in digital platforms, AI, digital twins, smart buildings, energy systems, or innovation ecosystems is important. Strong analytical, reading, and writing skills are required. Experience with literature reviews, qualitative analysis, technology mapping, or basic knowledge of APIs, data standards, or platform architecture would be an asset, but is not required.

Research area: This initiative will involve designing and deploying OHS training modules that not only comply with Canadian safety standards but also address language barriers that may hinder effective safety practices in multilingual workforces. We plan to collaborate with industry partners to identify gaps in existing OHS training, particularly in language accessibility, and to develop modules that are easily understandable to non-native English speakers.

Student roles:
The student will engage in software development, content creation, and user testing. They will assist in translating and localizing the software's user interface and OHS training content into multiple languages and adapt them based on cultural contexts. The role also includes conducting research to validate the effectiveness of the multilingual training modules.

Skills required:
Candidates should have strong programming skills, experience in educational software development, and an understanding of OHS principles. Additional languages and cross-cultural communication skills are an asset. The student should be highly motivated, possess excellent analytical skills, and be capable of working independently as well as part of a team.

Research area: Narendra Malalgoda is an Assistant Professor of Supply Chain Management at the Asper School of Business, University of Manitoba. He completed his Ph.D. in Transportation and Logistics, with emphasis on Logistics and Supply Chain Systems, in 2020 and holds an MSc in International Agribusiness, both from the North Dakota State University, USA. Before joining the Asper School of Business, Dr. Malalgoda completed his post-doctoral training in the Department of Agribusiness and Agricultural Economics at the UofM. Dr. Malalgoda is the Associates fellow in Supply Chain Management 2024-2027.

Student roles:
The student's primary role is to build and curate the national grocery store database that underpins the broader research program. Day-to-day, this involves gathering store location data from a range of sources, including open government datasets, retailer store locators, business directories, and existing partial inventories. The student will work systematically through Canada's provinces and territories to ensure comprehensive coverage, rather than leaving gaps in less-populated or remote regions.

Once data is collected, the student will clean and standardize it. This includes reconciling inconsistent store names, identifying and removing duplicates, correcting or completing addresses, and geocoding addresses into geographic coordinates. The student will develop and apply a classification scheme that sorts stores into meaningful categories—for example, full-service supermarkets, discount grocers, convenience stores, and specialty or ethnic food retailers — since these distinctions matter for later accessibility analysis.
The student will maintain clear documentation of the source of each data point and the processing steps applied, so the dataset is transparent and reproducible. They will perform quality-control checks, such as spot-verifying geocoded locations on a map and flagging anomalies for review.
Throughout, the student will collaborate closely with the supervisor and with the intern working on the GIS analysis stream, ensuring the database structure meets the needs of downstream spatial work. They will join regular check-ins to report progress, raise data challenges, and adjust priorities. Toward the end of the term, the student will deliver a clean, analysis-ready geospatial dataset, along with documentation and a short summary of methods and limitations.
This role offers hands-on experience in data curation and geospatial data preparation, foundational skills for any research career involving spatial or quantitative analysis, and a tangible, well-defined deliverable suited to a short internship.

Skills required:
The student should have basic familiarity with spreadsheets (Excel or Google Sheets) and be comfortable working with large datasets. Some exposure to data cleaning, handling duplicates, inconsistent formatting, and missing values is helpful. Basic understanding of geographic concepts (addresses, coordinates, postal codes) and introductory mapping or GIS tools is an asset but not required. Attention to detail, patience with repetitive tasks, and good documentation habits matter most. Familiarity with Python or R for data handling is beneficial but optional. Strong motivation and reliability are valued over advanced technical skills.

Research area: I am a management scholar, with a specific expertise in strategic management, sustainability management, and cross-sector partnerships. I am leading a large interdisciplinary team project that draws on and contributes to climate policy, management, accounting, urban/regional planning, environmental studies and sustainability science disciplines.

Student roles:
The student will support the research project as a research assistant. This may involve helping with data collection, helping with data analysis, conducting a systematic literature review, helping with report preparation, helping with knowledge mobilization, etc. The student will be trained on the skills needed to perform the relevant research tasks.

Skills required:
The following would be assets; none are required:
- An understanding of the climate emergency, climate mitigation and sources of GHG emissions.
- An understanding of one or more of the management aspects of this project: carbon accounting, sustainability reporting, municipal decision-making, Climate Related Financial Disclosure (TCFD) reporting, strategic planning, sustainability management, climate action, etc.
- Research-related skills such as statistical analysis, qualitative content analysis, report writing or knowledge mobilization.
-Strong communication skills in English and French

Research area: My research focuses on promoting sustainable food systems, with a particular emphasis on sustainable eating behaviours and food businesses. I conduct both qualitative and quantitative research at the intersection of sustainability, marketing, and public health and I am particularly interested in studying the promotion of sustainability through interventions and sustainable marketing, especially among critical sub-group populations such as young adults and children. With an interdisciplinary approach I strive to bridge concepts and insights from diverse research domains to create research that leverages academia.

Student roles:
The student will be responsible for conducting literature review, assist in creating guides for interviews and focus groups, participate in data collection and analysis, prepare reports and drafts for publication.

Skills required:
Background in food studies and nutrition
Prior experience in conducting interviews and moderating focus groups
Prior experience in working with qualitative data (such as thematic analysis)
Time management and communication skills
Ability ti take initiative and a team player

Research area: This research focuses on applied artificial intelligence, business analytics, and management education. It examines how large language models can be customized and grounded in domain-specific knowledge to support learning, decision-making, and skill development. The work integrates prompt engineering, retrieval-augmented generation, and evaluation frameworks to design AI systems tailored to business contexts, with applications in teaching, case analysis, and applied research.

Student roles:
The student will design and implement course-specific AI systems using large language models. They will begin by organizing and structuring course materials, including lecture content, case studies, and datasets, to support retrieval-based systems.

The student will develop prompt templates and workflows that guide the model to produce outputs aligned with course objectives. They will implement retrieval-augmented generation pipelines and test system performance across different tasks, such as case analysis and data interpretation.

The student will evaluate outputs using defined criteria, including accuracy, consistency, and pedagogical usefulness. They will refine system design based on testing and feedback.

Additional responsibilities include documenting system architecture, developing demonstration use cases, and contributing to a final report. The student will work independently on technical tasks while engaging in regular discussions on design, evaluation, and educational implications.

Skills required:
Students should have a background in business analytics, data science, computer science, or a related field. Experience with Python and familiarity with APIs or working with AI tools is required. Interest in education, instructional design, or business applications of AI is beneficial. Strong problem-solving skills and the ability to work with structured and unstructured data are important.

Research area: Dr. Will Zhao is an interdisciplinary scholar focusing on Innovation Research. He explores innovation in its myriad facets and manifestations, drawing insights from social sciences, humanities, and engineering. A significant part of his research involves examining emerging technologies, such as artificial intelligence and extended reality, in the context of business, education, and health. He uses a mix of qualitative and quantitative methods to dive deep into his interdisciplinary research questions, drawing on a toolkit that includes linguistic and semiotic analysis, statistical modeling, and machine learning. His research has appeared in impactful social science, engineering, and interdisciplinary journals.

Student roles:
The student will be primarily responsible for conducting literature reviews and analyzing data. They will work closely with the supervisor to develop and execute intervention strategies, document preliminary findings, and prepare reports.

Skills required:
The ideal intern should have an academic background in psychology (organizational behavior), movement therapy, peformance arts, arts-integration education, and/or innovation studies.
Familiarity with dance or somatic practices as an intervention is highly desirable.
The intern should have experience in conductin literature review and data analysis.
Proficiency in English is a must.
Knowledge of Chinese (Traditional) is a plus.

Research area: My research interests are in applied analytics & machine learning, business process improvement, and optimization. My current and past research focuses on cross-disciplinary approaches to address business improvement and optimization problems. For instance, the application of machine learning, evolutionary algorithms, combinatorial optimization, simulation modelling, Lean/Six Sigma, and autonomous decision-making to industrial/service process improvement, with a focus on process, people, and technology. Business Intelligence Data Analytics Data Visualization Machine Learning Text Mining - NLP Transformer-based models/LLMs for qualitative analysis Python Programming

Student roles:
The student research assistant will be responsible for:
- designing and implementing web scraping scripts to collect data from online job portals, industry reports, and professional social media platforms.
- applying NLP techniques to analyze textual data obtained from web scraping.
- analyzing the collected data to generate meaningful insights and visualize the results.
- conducting thorough literature reviews to contextualize the research within existing academic and industry knowledge.
- assisting in designing and conducting surveys and interviews to gather qualitative data.
- documenting research processes and findings in a clear and organized manner.

Skills required:
Technical Skills: Web Scraping (tools such as BeautifulSoup, Scrapy, or Selenium), understanding of HTML, CSS, and JavaScript to navigate and extract data from web pages, ability to handle different data formats (XML, CSV) and APIs for data extraction. Programming Languages (Python, particularly for web scraping and NLP tasks, transformer-based models, Pandas, SNS, Matplotlib); Basic understanding of NLP concepts and techniques.

Ability to conduct literature reviews, collect, organize, and manage large datasets.

Strong analytical skills to identify patterns, trends, and insights from data.

Strong written and verbal communication skills for documenting research processes and presenting findings clearly.

Research area: My research interests are in applied analytics, machine learning, supply chain management, business process improvement, and sustainability-oriented decision-making. My work uses cross-disciplinary methods such as text mining, Natural Language Processing, data visualization, and analytical modelling to study how organizations improve resilience, sustainability, digital transformation, and operational performance. This project connects business analytics with supply chain capability development, using industry reports as empirical sources to understand sector-specific priorities, capability gaps, and alignment patterns across the technology, people, information processing, and knowledge-sharing dimensions. Business Intelligence Data Analytics Data Visualization Machine Learning Text Mining - NLP Transformer-based models/LLMs for qualitative analysis Python Programming

Student roles:
The student research assistant will be responsible for:
- designing and implementing web scraping scripts to collect data from company reports, industry reports, and professional social media platforms.
- helping develop a sector taxonomy for classifying industry reports by industry context;
- helping develop a capability taxonomy for classifying reports or paragraphs by themes such as resilience, sustainability, digitization, risk, circularity, visibility, traceability, collaboration, and workforce capability;
- cleaning and organizing report-level and paragraph-level metadata;
- applying text mining, NLP, and topic modelling techniques to sector-specific report subsets;
- calculating and comparing orientation scores across sectors and capability categories;
- developing visual outputs such as heat maps, radar charts, frequency distributions, and comparative tables;
- mapping sector-specific themes to HI-TOP–SCM components;
- documenting methods, assumptions, classification rules, and analytical findings in a clear and reproducible manner.

Skills required:
The student should have an interest in business analytics, supply chain management, sustainability, and digital transformation. Technical skills in Python, Pandas, data cleaning, web scraping, text mining, NLP, topic modelling, and data visualization are desirable. Familiarity with TF-IDF, transformer-based models, zero-shot classification, or qualitative coding would be an asset. The student should be able to conduct (some) literature reviews, organize and manage datasets, classify documents, interpret patterns in textual data, and communicate findings clearly through tables, visualizations, and written summaries.

Research area: As a full professor of entrepreneurship and innovation management at Polytechnique Montréal, I have spent the last two decades at the intersection of research, innovation, and public policy. My academic and applied work focuses on the strategic management of technology, with a particular emphasis on ecosystems strategic thinking. My work bridges engineering, management, and public policy, with a particular emphasis on how intangible assets, such as patents, scientific knowledge, and entrepreneurial capabilities, can be mobilized to enhance firm-level productivity.

Student roles:
The student will assist the research team in collecting, cleaning, and structuring data for analysis, as required by any text mining research project. Once this collaborative data organization work is completed with the rest of the team, the student will conduct an individual preliminary data analysis to prepare a final internship report in the form of a scientific article.

Skills required:
The candidate should have some knowledge about the literature on "ecosystems" in business and management. Previous experience with LLM (Large-language Models) and AI-powered text analysis is an asset.

Research area: My research sits at the intersection of careers, aging, and human resource management, with a focus on how older adults navigate work and transition out of it. I examine bridge employment, retirement adjustment, and the economic engagement of mature workers, with particular attention to rural and underserved communities in Atlantic Canada. My work explores how organizations and communities can better leverage the skills and experience of aging workers, and how structural factors shape employment outcomes across the life course. Recent projects extend this focus to succession planning, workforce sustainability, and the gendered dimensions of career transitions and retirement.

Student roles:
The Mitacs intern will contribute directly to all four research goals over 12 weeks, gaining hands-on experience in community-engaged research, stakeholder engagement, and applied qualitative methods.
Weeks 1–2: Orientation and Preparation
The intern will review relevant literature on succession planning and rural entrepreneurship, familiarize themselves with the project's goals and community context, and assist in refining the interview guide and mapping framework used throughout the project.
Weeks 3–5: Mapping the Succession Landscape (Goal 1)

The intern will identify and document small businesses, community organizations, and existing succession-related programs across target rural communities. This involves desk research, telephone outreach, and compiling a structured inventory of relevant stakeholders, supports, and gaps.
Weeks 6–8: Data Collection — Barriers and Knowledge Transfer (Goals 2 and 3)

The intern will coordinate interview scheduling, prepare consent and briefing materials, and participate in qualitative interviews with business owners, older workers, and community representatives. They will take detailed notes, manage recordings, and assist with transcription.
Weeks 9–10: Data Analysis (Goals 2 and 3)

Under faculty supervision, the intern will contribute to thematic analysis of interview data, including coding, identifying emerging themes, and documenting key findings related to succession barriers and knowledge transfer practices.
Weeks 11–12: Developing and Sharing Strategies (Goal 4)

The intern will assist in translating findings into practical recommendations and co-producing a community-facing summary report and stakeholder presentation. Materials will be tailored for entrepreneurs, regional development agencies, and policymakers.
Throughout the project, the intern will develop transferable skills in qualitative research, community engagement, and knowledge translation. Strong contributors will have opportunities to co-author academic publications and participate in conference presentations.

Skills required:
The ideal candidate will have an interdisciplinary background in business, human resource management, sociology, or a related social science, with interest in entrepreneurship, aging, or rural community development. Strong written and verbal communication skills are essential, as is the ability to engage professionally with diverse stakeholders including small business owners, older workers, and community organizations. Familiarity with qualitative research methods such as interviewing or thematic analysis is an asset but not required. The student should be organized, self-directed, and comfortable contributing to applied, community-based research where findings are expected to inform practical decisions for entrepreneurs and regional policymakers.

Research area: My research focuses on responsible artificial intelligence adoption, digital transformation, and organizational technology governance. I study how organizations can adopt emerging technologies while maintaining trust, ethical use, productivity, cybersecurity awareness, and employee confidence. This project examines how workplace AI policies influence employees’ willingness to disclose AI use and how managers can design governance practices that encourage responsible adoption rather than fear, shame, or hidden use.

Student roles:
The student will support the development of a research project on workplace AI governance, with the goal of contributing to a journal manuscript. Their role will include reviewing academic and practitioner literature on responsible AI, AI-use policies, employee disclosure, psychological safety, trust, organizational control, technology governance, and digital transformation. The student will help summarize key theories and findings in a structured literature review table.

The student will also assist in developing and comparing different workplace AI policy scenarios, such as mandatory disclosure, optional disclosure, safe-harbor disclosure, AI-use normalization by managers, training-first policy, and punitive policy. They may help prepare scenario materials, refine survey questions, identify outcome measures, and support the development of a conceptual framework explaining how policy design influences employee disclosure, trust, and responsible AI use.

Depending on project timing and ethics approval, the student may assist with survey preparation, pilot testing, data organization, descriptive analysis, and the development of tables or figures. The student will meet regularly with the supervisor to discuss theory development, research design, interpretation of findings, and manuscript development.

By the end of the internship, the student is expected to contribute to a literature summary, policy-scenario design document, conceptual framework, preliminary analysis materials, and sections of a draft manuscript. The overall aim is to advance the project toward submission to a peer-reviewed journal in organizational behavior, human resource management, information systems, business ethics, or responsible AI governance.

Skills required:
The student should have an interest in management, organizational behavior, human resource management, business ethics, information systems, responsible AI, or public policy. Experience with literature reviews, survey design, policy analysis, Qualtrics, Excel, SPSS, or basic data analysis is helpful but not required. The student should have strong writing skills, analytical thinking, attention to detail, and an interest in how organizations can design fair and effective policies for generative AI use at work.

Research area: My research focuses on the development and application of quantitative and analytical methods to support decision-making in complex systems. I work in the areas of optimization, decision theory, multi-criteria decision-making (MCDM), inventory modelling, time series analysis, fuzzy set theory, and supply chain analytics. My research aims to develop innovative models and decision-support frameworks that help organizations improve efficiency, resilience, sustainability, and operational performance under uncertainty. By integrating operations research, artificial intelligence, forecasting techniques, and uncertainty modelling, I seek to address real-world challenges in supply chain management, logistics, healthcare operations, and business decision-making while bridging the gap between theory and practice.

Student roles:
The student will play an active role in all phases of the research project, from literature review to data analysis and the development of practical recommendations. The primary responsibility will be to investigate the differences between quoted price and effective price in supplier evaluation and to examine how tariffs influence procurement and supply chain decisions.
The student will begin by conducting a comprehensive review of academic and industry literature related to supplier selection, total cost of ownership, effective pricing, tariffs, global sourcing, and supply chain risk management. This review will help identify key factors that contribute to the true cost of procurement beyond the supplier's quoted price.
The student will collect, organize, and analyze relevant data from industry reports, public sources, case studies, and company information where available. They will assist in identifying and quantifying factors such as transportation costs, inventory carrying costs, quality-related costs, lead-time variability, tariffs, and supply chain risks that contribute to the effective price. Using analytical and quantitative methods, the student will evaluate how these factors affect supplier rankings and sourcing decisions under different trade and tariff scenarios.
In addition, the student will support the development of decision-support models and frameworks that organizations can use to compare suppliers based on overall value rather than purchase price alone. This may involve conducting scenario analyses, sensitivity analyses, and comparative evaluations to assess the impact of changing tariff rates and supply chain conditions.
The student will be expected to document research findings, prepare technical reports, develop presentations, and communicate effective results to both academic and industry audiences. Regular meetings with the supervisor will be required to discuss progress, review results, and refine the research direction. Through this project, the student will gain valuable experience in supply chain analysis, procurement strategy, decision-making, and applied research while contributing to a topic

Skills required:
The ideal student should have a background in Supply Chain Management, Operations Management, Business Analytics, Industrial Engineering, Economics, or a related discipline. The student should possess strong analytical and problem-solving skills and be comfortable working with data, spreadsheets, and quantitative analysis. Knowledge of procurement, supplier selection, logistics, inventory management, and international trade is desirable. Experience with Excel, statistical analysis, optimization, or decision-making techniques is an asset but not required. Strong written and verbal communication skills are important, as the project involves research, data analysis, report writing, and presenting recommendations to support effective supply chain decision-making.

Research area: This research area is at the intersection of data science, analytics, machine learning, quantitative modeling and artificial intelligence with personalized and adaptive learning. We are using both quantitative and cognitive algorithms to improve education and learning. Being able to construct interactive courses and educational materials that adapt to learning patterns of each individual automatically, and improve speed of learning as well as understanding of learning materials is the key.

Student roles:
A student will be analyzing data and applying algorithms to develop a cognitive software tool for personalized adaptive learning. The tool would automatically re-arrange slides and learning materials, decide whether to add quizzes and additional interactive materials to improve learning of an individual taking an online course and motivate people to study. Writing code, preferably in Python, to apply quantitative, machine learning and artificial intelligence algorithms will be integral part of the student role.

Skills required:
Student background would preferably be in a quantitative field such as data science, information technology, computer science, engineering, mathematics, statistics, economics or education sciences, but students with other backgrounds that have basic knowledge of data analytics are welcome to apply as well. Required skills are basic mathematics and statistics, understanding of algorithms and learning process, and ability to program in Python, R or Matlab. Familiarity with visualization tools such as D3 and Java Script would be an asset.

Research area: Our laboratory bridges the gap between academic innovation and real-world adoption across trustworthy technology, security operations, and human readiness. A critical dimension of our research program is technology transfer optimization and research entrepreneurship. We study the systematic bottlenecks that cause high-value cybersecurity research to stall inside the university ecosystem. By analyzing commercialization frameworks, intellectual property packaging, and Technology Readiness Level (TRL) translation methodologies, we develop reproducible frameworks for market-entry. We don't just ask what technologies to build; we research how to structurally extract, mature, and license academic software assets for permanent, independent industrial adoption.

Student roles:
The selected intern will serve as a Tech Transfer Analyst and Research Venture Lead, spearheading the optimization of our laboratory’s commercialization pipeline. Over the 12-week internship, the student will direct a systematic portfolio audit and framework experiment:

Initially, the intern will interview lab researchers, review active source code repositories, and catalog our existing software assets. They will analyze why past innovations failed to transition, isolating specific technical or operational friction points. The student will then map these assets against standard TRL benchmarks, documenting the structural deficits preventing industry adoption.

The core research experiment requires the student to design and validate a repeatable "Cybersecurity Tech-Transfer Scoring Engine." This tool will ingest variables like code dependencies, deployment complexity, and target audience alignment to output an empirical "Commercial Readiness Score." To validate the framework, the student will select 2 to 3 promising lab innovations and execute an end-to-end "translation mock-run"—drafting technical value propositions, identifying precise industry insertion points, and defining the exact architectural refactoring required for commercial deployment.

The intern will present their asset reviews and methodology updates during weekly laboratory syncs, gathering feedback from senior engineers and institutional TTO representatives. Additionally, they will author the final "Lab-to-Market Playbook," establishing the standard operational guidelines for packaging future laboratory software.

By week 12, the student will deliver a finalized asset portfolio map and a plug-and-play evaluation toolkit. This role offers an immersive experience at the intersection of deep tech and venture development, directly defining how academic breakthroughs scale into permanent, self-sustaining industrial solutions.

Skills required:
Applicants should be pursuing an interdisciplinary or technical degree in Management of Technology, Engineering Entrepreneurship, Computer Science, or Information Systems. Candidates must possess strong technical literacy to read and understand code repositories, system architectures, and academic engineering papers. Familiarity with the Technology Readiness Level (TRL) framework, software licensing models, or product management principles is highly desirable. We seek analytical, entrepreneurially-minded individuals who can interface comfortably between engineering data and business strategy, possessing the structured thinking required to evaluate market friction and translate complex technical innovations into viable product opportunities.

Research area: My research interests are in applied analytics, machine learning, supply chain management, business process improvement, and sustainability-oriented decision-making. My work uses cross-disciplinary methods such as text mining, Natural Language Processing, data visualization, and analytical modelling to study how organizations improve resilience, sustainability, digital transformation, and operational performance. This project connects business analytics with supply chain capability development, using industry reports as empirical sources to understand sector-specific priorities, capability gaps, and alignment patterns across the technology, people, information processing, and knowledge-sharing dimensions. Business Intelligence Data Analytics Data Visualization Machine Learning Text Mining - NLP Transformer-based models/LLMs for qualitative analysis Python Programming

Student roles:
The student research assistant will be responsible for:
- designing and implementing web scraping scripts to collect data from company reports, industry reports, and professional social media platforms.
- helping develop a taxonomy that classifies reports into upstream, midstream, downstream, and cross-cutting supply chain positions;
- assigning reports to supply chain positions using sector metadata, report content, title information, publisher information, keywords, and model-assisted classification;
- preparing report-level and paragraph-level datasets for analysis;
- applying zero-shot classification, text mining, topic modelling, and other NLP techniques to compare discourse across supply chain stages;
- developing visual outputs such as supply chain maps, heat maps, thematic flow maps, and HI-TOP–SCM alignment matrices;
- comparing how upstream, midstream, and downstream reports emphasize resilience, sustainability, digitization, traceability, visibility, collaboration, and workforce capability;
- documenting classification logic, analytical procedures, findings, and limitations in a clear and reproducible manner.

Skills required:
The student should have an interest in business analytics, supply chain management, sustainability, and digital transformation. Technical skills in Python, Pandas, data cleaning, web scraping, text mining, NLP, topic modelling, and data visualization are desirable. Familiarity with TF-IDF, transformer-based models, zero-shot classification, or qualitative coding would be an asset. The student should be able to conduct (some) literature reviews, organize and manage datasets, classify documents, interpret patterns in textual data, and communicate findings clearly through tables, visualizations, and written summaries.

Research area: This research sits at the intersection of innovation strategy, entrepreneurship, and network science applied to emerging quantum technology markets. It investigates how new industrial ecosystems form around quantum startups, how legitimacy and credibility are built among investors, universities, and policy actors, and how partnership patterns shape market creation. The work emphasizes quantitative network analytics, relational data, and interpretive synthesis to explain structural features of early-stage quantum industry development. Students will engage with public records, startup databases, and bibliometric or investment data to construct reproducible network representations. The goal is actionable insight for scholars and ecosystem stakeholders.

Student roles:
The Globalink Research Intern will take substantial responsibility for building the empirical backbone of the project. Early weeks will focus on scoping data sources, designing coding schemes for nodes and edges, and validating extraction workflows with the supervisor. The intern will implement reproducible pipelines to ingest, clean, and harmonize records from multiple public sources, maintain a versioned methodology note, and track decisions about ambiguous cases (e.g., name disambiguation, duplicate entities, missing attributes).

Mid-project work centers on network construction and analysis: building graph objects, computing descriptive statistics, running community detection and centrality analyses, and iterating on visualizations that reveal key structures without over-interpreting noise. The intern will test sensitivity to reasonable specification choices, document limitations candidly, and prepare tables and figures suitable for reporting. Where appropriate, the intern will support brief comparative analyses across regions or time periods and help translate findings into clear narratives about how startups connect to investors, universities, and policy actors.

In later weeks, emphasis shifts to integration and communication: drafting summaries that link quantitative patterns to qualitative interpretation about market creation, preparing a concise results package, and revising based on supervisor feedback. The intern will participate in regular meetings, present interim results, and maintain organized project files so analyses can be reproduced. Throughout, the intern is expected to uphold ethical data practices, respect intellectual property norms for public information, and communicate proactively about timeline risks, tool needs, and any uncertainties encountered in the work. The role is structured to build technical depth, research communication skills, and professional habits aligned with responsible innovation research.

Skills required:
Ideal candidates bring coursework or experience in analytics, applied statistics, data science, or management science, and comfort with network analysis concepts such as graph representations, tie strength, and basic centrality ideas. Strong programming skills in Python or R are important for reproducible data cleaning, graph construction, statistical summaries, and visualization. Familiarity with bibliometric sources, startup or funding databases, or innovation policy literature is helpful but not required. The student should be detail-oriented, careful with data provenance, able to document methods clearly, and able to communicate results in plain language for non-specialist readers.

Research area: My research primarily focuses on the optimization and management of complex systems within the fields of logistics, supply chain management, and operations research. My work often integrates mathematical modeling and computer simulation to enhance efficiency in various industrial sectors, mostly in sustainable logistics, urban freight transport, and manufacturing operations scheduling, found in real-world settings. I seek to balance economic productivity with environmental sustainability and social responsibility, contributing to the development of "smart" and resilient industrial and service systems.

Student roles:
The student will develop mathematical and simulation models, run computer experiments on different operational scenarios, and analyze input and output data from the simulations

Skills required:
Knowledge in operations research, systems modeling, with practical experience with optimization and/or discrete-event or agent-based simulation software (Pyomo, Arena, Simio). A background in Logistics and Supply Chain Management, and risk management is essential. Proficiency in data analysis tools (like Python or R) and the ability to synthesize academic literature. Good communication skills, both oral and written.

Research area: My research primarily focuses on the optimization and management of complex systems within the fields of logistics, supply chain management, and operations research. My work often integrates mathematical modeling and computer simulation to enhance efficiency in various industrial sectors, mostly in sustainable logistics, urban freight transport, and manufacturing operations scheduling, found in real-world settings. I seek to balance economic productivity with environmental sustainability and social responsibility, contributing to the development of "smart" and resilient industrial and service systems.

Student roles:
The student will develop mathematical and simulation models, run computer experiments on different operational scenarios, and analyze input and output data from the simulations

Skills required:
Knowledge in operations research, systems modeling, with practical experience with optimization and/or discrete-event or agent-based simulation software (Pyomo, Arena, Simio). A background in Logistics and Supply Chain Management, and risk management is essential. Proficiency in data analysis tools (like Python or R) and the ability to synthesize academic literature. Good communication skills, both oral and written.

Research area: My research focuses on entrepreneurship, innovation, entrepreneurial ecosystems, and the role of collaboration in fostering economic development. A key area of my work examines how organizations leverage external knowledge, partnerships, and networks to enhance innovation and competitiveness. Through research on open innovation, university-industry collaboration, and entrepreneurial ecosystems, I explore how businesses, academic institutions, and other stakeholders can work together to create innovative solutions and strengthen regional economies. My research aims to generate practical insights that support innovation, talent development, and sustainable economic growth.

Student roles:
The student will work closely with the faculty supervisor on a research project examining open innovation, university-industry collaboration, and regional economic development. The position is designed to provide students with an opportunity to apply their disciplinary knowledge while gaining exposure to entrepreneurship, innovation management, policy, and ecosystem research.

The student will contribute to literature reviews, environmental scans, and the identification of best practices related to open innovation and collaborative partnerships. Responsibilities may include collecting and organizing secondary data, mapping innovation stakeholders, supporting interview recruitment, participating in interviews, conducting background research on innovation initiatives, and assisting with qualitative and quantitative data analysis.

The student will also contribute to the preparation of research reports, policy briefs, presentations, and knowledge mobilization materials for academic and practitioner audiences. A key expectation is that students will bring their disciplinary perspective to the project while remaining open to learning from complementary fields such as entrepreneurship, innovation management, economic development, and public policy.

Strong communication and writing skills are important, as students will contribute to research summaries, stakeholder reports, presentations, and scholarly outputs. Students may also interact with business leaders, innovation organizations, universities, and community stakeholders to better understand the opportunities and challenges associated with collaborative innovation.

Throughout the project, students will gain experience in interdisciplinary research, stakeholder engagement, innovation ecosystem analysis, and applied research methods. They will participate in research meetings and may have opportunities to contribute to conference presentations, practitioner reports, teaching materials, and academic publications. The project is particularly well suited for students interested in future graduate studies in entrepreneurship, innovation, public policy, management, or technology commercialization.

Skills required:
Applicants should have a strong academic background in Entrepreneurship, Business, Management, Public Policy, Economics, Innovation Studies, Technology Innovation, Engineering Management, Information Systems, or a related discipline. We welcome students who have demonstrated academic excellence in their field and are interested in exploring innovation, entrepreneurship, collaboration, and regional economic development. Strong communication, analytical, and academic writing skills are essential. Experience with literature reviews, stakeholder engagement, surveys, interviews, or research methods is beneficial but not required. Curiosity, initiative, and a willingness to engage with interdisciplinary research are highly valued.

Research area: My research focuses on the development and application of quantitative and analytical methods to support decision-making in complex systems. I work in the areas of optimization, decision theory, multi-criteria decision-making (MCDM), inventory modelling, time series analysis, fuzzy set theory, and supply chain analytics. My research aims to develop innovative models and decision-support frameworks that help organizations improve efficiency, resilience, sustainability, and operational performance under uncertainty. By integrating operations research, artificial intelligence, forecasting techniques, and uncertainty modelling, I seek to address real-world challenges in supply chain management, logistics, healthcare operations, and business decision-making while bridging the gap between theory and practice.

Student roles:
The student will be involved in all phases of the research process, from problem identification to the dissemination of results. Responsibilities will include conducting comprehensive literature reviews to identify research gaps and emerging trends, collecting, cleaning, and analyzing quantitative and qualitative data, and developing mathematical, statistical, and computational models to address complex decision-making problems. The student will assist in designing and implementing optimization models under both deterministic and uncertain environments. The role will also involve performing computational experiments, sensitivity analyses, scenario evaluations, and model validation using real-world datasets from supply chain, logistics, healthcare, sustainability, and business applications.
In addition, the student will contribute to the development of innovative methodologies by integrating operations research techniques. The student will be expected to interpret research findings, generate managerial insights, and translate technical results into practical recommendations for decision-makers. Responsibilities will also include preparing research reports and presenting findings at research group meetings. The successful candidate should demonstrate strong analytical and problem-solving abilities, intellectual curiosity, initiative, and the capacity to work both independently and collaboratively within an interdisciplinary research environment. Through this role, the student will gain valuable experience in advanced analytical methods, academic research, scientific writing, and industry-focused problem-solving while contributing to impactful research in supply chain management, logistics, healthcare operations, sustainability, and business decision-making

Skills required:
Students should have a strong background in quantitative methods, mathematics, statistics, OR industrial engineering, supply chain management, business analytics, computer science, or a related discipline. Experience in optimization techniques, decision analysis, data analytics, machine learning, forecasting, inventory management, or multi-criteria decision-making methods would be highly beneficial. The student should be comfortable working with mathematical models and have programming experience. Knowledge of statistical analysis, artificial intelligence, and fuzzy set theory is an asset. Candidates should also demonstrate strong communication skills, independence, and a willingness to engage in interdisciplinary research that addresses practical challenges in SCM, logistics, healthcare systems, and business operations.

Research area: This project will be overseen by two professors, Dr. Greg King and Dr. Clark Banack. Dr. King is an environmental scientist with a strong interest in contributing toward sustainable communities, with his research focusing on urban greenspace and its associated environmental, social and economic benefits. He also teaches courses focused on sustainability application specifically integrating ecology into cities. Dr. Banack studies political science and is the Director of the Alberta Centre for Sustainable Rural Communities. He specializes in the areas of western Canadian politics, religion and politics, and rural-urban issues. His research focuses on multiple ways to build sustainable communities.

Student roles:
We are especially interested in partnering with a student with a passion for action-oriented research and informing concrete actions aimed at enhancing sustainability. The successful applicant on this research project will play an active role in office and community-based research settings. The student will spend about one-half of their time collecting data in the community. The primary research locations will be the City of Camrose and the adjacent Camrose County, looking at the potential opportunities for synergies and partnerships on sustainable initiatives on but not limited to topics such as energy, waste, water and food. Data collection may include work on relevant existing primary and secondary literature, as well as conducting interviews and surveys with local stakeholders. The successful applicant will write summary reports for partners.

In addition the student will be exposed to other field and literature research that is ongoing at the Augustana Campus of University of Alberta. The student will also have the opportunity to present their work at the research assistant symposium on campus in August as well as other potential venues for members of the public. Each student brings their own skills, experience and interests and we try and tailor projects to areas of strength while encouraging students to develop new skills and approaches.

Skills required:
1. Background and/or interest in sustainability studies, environmental studies, geography, public policy, or related field;
2. Strong interpersonal skills and comfortable working with the public and potentially conducting in-person interviews;
3. Attention to detail, hard-working, good communication skills and a team player;
4. Experience and willingness to conduct literature reviews, interviews and surveys;
5. Willing to be involved in diverse project activities and learn new skills

Research area: My research focuses on digital transformation, digital platforms, and organizational adoption of emerging technologies. I study how organizations implement and use AI-enabled systems, with attention to human–AI interaction, user engagement, change management, trust, and governance. In a current research project on AI-enabled digital twins for building decarbonization, my work examines how cognitive digital twins and AI-enabled decision-support tools can be socialized within organizations and how users interact with these systems in contexts such as building management, energy efficiency, and decarbonization.

Student roles:
The student will support an exploratory research project on perceived agency in AI-enabled work environments. Their role will include reviewing literature on human–AI interaction, perceived agency, trust, explainability, autonomy, and AI-supported decision-making. They will help synthesize key concepts and identify how perceived agency may influence user engagement with cognitive digital twins and related AI tools. The student may assist in developing a conceptual framework that links AI system features, user perceptions, organizational context, and adoption outcomes. Depending on project progress, they may also help design interview or survey questions for users such as building operators, facility managers, engineers, or organizational decision-makers. Expected outputs may include an annotated bibliography, literature review summary, conceptual framework, draft research instruments, and a short research report. The student will meet regularly with the supervisor and research team and will receive training in academic writing, qualitative analysis, and theory development.

Skills required:
The student should have a background in information systems, management, human–computer interaction, organizational studies or a related field. Interest in AI, digital transformation, human–AI interaction, or technology adoption is important. Strong reading, analytical, and writing skills are required. Experience with literature reviews, qualitative research, surveys, interviews, or basic data analysis would be an asset, but is not required.

Research area: This research focuses on applied artificial intelligence, business process analytics, and decision support systems. It integrates large language models, process mining, and agent-based systems to analyze, automate, and improve organizational workflows. The work emphasizes practical, interpretable AI systems that support managerial decision-making, with applications in operations, marketing, and service delivery. A key focus is understanding how AI can move beyond task automation toward adaptive, self-improving business processes.

Student roles:
The student will design, build, and evaluate AI-supported business workflows. They will begin by identifying and mapping selected business processes, including key steps, decision points, and data inputs. The student will then develop prototypes that integrate AI agents into these workflows using available tools and APIs.

Responsibilities include implementing task automation components, developing logic for agent interactions, and testing how workflows perform under different scenarios. The student will evaluate system outputs, identify limitations, and iteratively refine the design.

The student will also conduct comparative analysis between traditional and AI-augmented processes, focusing on efficiency, usability, and decision quality. They will document methods, maintain reproducible workflows, and contribute to final outputs including reports and demonstrations.

Regular meetings will support progress and refinement. The student will work independently on technical implementation while engaging in collaborative discussion on design and interpretation. This project provides experience at the intersection of AI, business analytics, and process innovation.

Skills required:
Students should have a background in business analytics, data science, computer science, or a related field. Experience with Python is required, along with familiarity with APIs or working with AI tools. Basic understanding of data analysis and workflow design is beneficial. Interest in artificial intelligence applications in business is essential. Strong problem-solving skills and the ability to work with both structured and unstructured data are important.

Research area: This research area focuses on the early-stage commercialization of neurotechnology, specifically transcranial ultrasound neuromodulation systems. It integrates market analysis, competitive landscape assessment, and strategic positioning to support the translation of advanced neuromodulation technology into clinical and commercial applications. Key activities include developing investor- and partner-facing communication materials, articulating technology value propositions aligned with clinical needs, and identifying non-dilutive funding opportunities. The work bridges technical innovation and business strategy, emphasizing market readiness, external engagement, and sustainable growth pathways for emerging medtech startups in the neurotechnology space.

Student roles:
The student will play a key role in supporting the commercialization and external engagement efforts for a transcranial ultrasound neuromodulation startup. Leveraging a background in engineering or business, the student will conduct independent research on the competitive and market landscape, synthesizing insights to inform strategic positioning. They will help translate complex technical capabilities into clear, compelling narratives through written and visual materials tailored for investors, partners, and other stakeholders. In addition, the student will explore non-dilutive funding opportunities, including grants and public programs, contributing to the company’s growth strategy. This role provides hands-on exposure to medtech commercialization, entrepreneurship, and the intersection of technology and business decision-making.

Skills required:
Suggested student background:
• Background in biomedical engineering with a strong interest in business, or in business with a strong interest in technology
• Interest in medical technology commercialization or entrepreneurship
• Ability to research and synthesize competitive and market information
• Strong written and visual communication skills
• Comfort translating technical concepts into non-technical language
• Familiarity with basic business, marketing, or innovation frameworks
• Initiative and independent research skills
• Interest in grants, funding programs, or startup ecosystems

Research area: My research focuses on project management, technology transfer, and innovation in complex science- and engineering-based contexts. I study how project management practices can support universities, researchers, and technology transfer offices in structuring uncertain innovation projects, particularly in aviation and advanced mobility technologies.

Student roles:
The intern will contribute to a comparative qualitative study on university technology transfer in aviation-related technologies. The role includes supporting the identification of potential cases across selected countries with relevant aviation innovation ecosystems; mapping participants such as researchers, technology transfer office professionals, founders, project managers, or partners; organizing case information; preparing data collection materials; and supporting document analysis.
The intern may also contribute to interview preparation, data coding, analytical matrices, and preliminary within-case and cross-case analysis. The two interns will work collaboratively to build a structured empirical database and identify patterns regarding how project management practices are used, adapted, or neglected in university technology transfer projects. Their work will support future academic publications and practical recommendations for universities and innovation ecosystems.

Skills required:
The student should have a background in project management, engineering, management, innovation studies, entrepreneurship, technology transfer, or a related field. Skills in qualitative research, literature review, interview preparation, document analysis, data organization, thematic coding, scientific writing, and interest in aviation or advanced technologies are assets.

Research area: This research focuses on business analytics, artificial intelligence, and emerging technologies, with an emphasis on technology adoption and innovation readiness. It integrates natural language processing, machine learning, and data-driven decision-making to assess how organizations signal capability, intent, and preparedness for new technologies. A key area of interest is the commercialization and adoption of quantum technologies, including how firms position themselves within evolving innovation ecosystems and how readiness can be measured using observable data.

Student roles:
The student will play a central role in data collection, model development, and analysis. They will identify and compile relevant datasets from public sources, including job postings, company descriptions, and investment data. The student will preprocess and clean data, and implement natural language processing techniques to extract relevant signals.

They will design and test features that capture different aspects of quantum readiness, and contribute to the development of a composite scoring model. This will involve exploratory data analysis, model evaluation, and iterative refinement.

The student will also support the creation of visualizations and a prototype dashboard to communicate findings. Throughout the project, they will document methods, maintain reproducible workflows, and contribute to written outputs, including a final report and potential research dissemination.

Regular meetings will be held to discuss progress, troubleshoot challenges, and refine research direction. The student will work both independently and collaboratively, gaining experience in applied AI, innovation analytics, and emerging technology research.

Skills required:
Students should have a background in business analytics, data science, computer science, or a related field. Experience with Python, data cleaning, and basic machine learning is required. Familiarity with natural language processing, web scraping, or working with unstructured text data is an asset. Strong analytical thinking, attention to detail, and the ability to work with real-world datasets are important.

Research area: Logistique et transport de l’Industrie 4.0 Gestion de la chaîne de valeur/d'approvisionnement et des opérations Mécanisme de coordination (collaboration) interentreprises et modèle d'affaires Outil d'aide à la décision Économie circulaire Réduction des émissions de gaz à effet de serre

Student roles:
Bien que des rencontres régulières auront lieu entre le/la stagiaire et le superviseur, il/elle jouera un rôle de premier plan dans l’organisation et la réalisation de ses travaux de recherche afin de mener à terme avec succès et dans les délais impartis, le stage. Ainsi, le/la stagiaire sera responsable du traitement des données puis de l’analyse et de la synthèse des résultats. Appuyé par son superviseur et des lectures ciblées de la littérature, il/elle sera appelé à mettre en application dans les cas d'étude des composantes connues, mais également à faire preuve de créativité par la proposition/adaptation d’alternatives novatrices. Le stage pourra inclure la participation à des activités scientifiques (selon agenda des opportunités) ainsi que des échanges avec étudiant.e gradué.e et/ou les partenaires industriels impliqués dans un cas.

Skills required:
Faire preuve d’autonomie, d’initiative, d’esprit de synthèse et de rigueur dans la réalisation des travaux de recherche.
Maîtrise de Microsoft Excel et Access (ou des logiciels similaires plus avancés), à la fois pour le traitement et l’analyse des données que pour la préparation de tableaux et graphiques présentant les résultats.
La lecture de publications en langue anglaise ainsi qu'une expérience professionnelle et/ou une connaissance théorique dans le domaine de la logistique/transport sont des atouts.

Research area: Logistique et transport de l’Industrie 4.0 Gestion de la chaîne de valeur/d'approvisionnement et des opérations Mécanisme de coordination (collaboration) interentreprises et modèle d'affaires Outil d'aide à la décision Économie circulaire Réduction des émissions de gaz à effet de serre

Student roles:
Bien que des rencontres régulières auront lieu entre le/la stagiaire et le superviseur, il/elle jouera un rôle de premier plan dans l’organisation et la réalisation de ses travaux de recherche afin de mener à terme avec succès et dans les délais impartis, le stage. Ainsi, le/la stagiaire sera responsable du traitement des données puis de l’analyse et de la synthèse des résultats. Appuyé par son superviseur et des lectures ciblées de la littérature, il/elle sera appelé à mettre en application dans les cas d'étude des composantes connues, mais également à faire preuve de créativité par la proposition/adaptation d’alternatives novatrices. Le stage pourra inclure la participation à des activités scientifiques (selon l'agenda des opportunités) ainsi que des échanges avec un.e étudiant.e graduée. et/ou les partenaires industriels impliqués dans un cas.

Skills required:
Faire preuve d’autonomie, d’initiative, d’esprit de synthèse et de rigueur dans la réalisation des travaux de recherche.
Maîtrise de Microsoft Excel et Access (ou des logiciels similaires plus avancés), à la fois pour le traitement et l’analyse des données que pour la préparation de tableaux et graphiques présentant les résultats.
Connaissances élémentaires en programmation (script), la lecture de publications en langue anglaise ainsi qu'une expérience professionnelle et/ou une connaissance théorique dans le domaine de la logistique/transport sont des atouts.

Research area: I study the relationship between small organizations and organizations that provide technological support, specifically to implement artificial intelligence and other digital technologies. In Canada, there are several government-sponsored "digital acceleration and adoption programs" that provide funds for small organizations to work with a third party when the organizations lack the skills to implement AI on their own. I study this context, and in particular, how the relationship between these actors leads to resistance to technological change. This resistance is both a limiter and, sometimes, a value creator since it drives the organization to go further and learn.

Student roles:
The project involves analyzing interview transcripts and participating in open-ended, inductive coding alongside the graduate students assigned to this project (two doctoral students and possibly one master’s student). A summary guide of the concepts under study (resistance to technological change, change management) as well as training provided by the supervisor and the graduate students will be offered. Since open coding can be subjective, there is always more than one person assigned to each transcript. The intern will be one of these individuals.
Occasionally, the intern may be asked to assist with transcribing the interview recording or to help take notes during the interview. Most interviews will be in English, but some may be in French. The internship therefore includes training in qualitative text analysis methods, including the NVivo software, as well as training in qualitative data collection (interviews, ethical guidelines, best practices, etc.).
The intern will also be invited to participate in the preparation of scientific communications (reports, posters) and communications for partner organizations (infographics, summaries).
An office will be provided for the intern near the supervisor’s office, and the intern is expected to participate in one-on-one meetings with the supervisor or a graduate student, as well as in the research group’s monthly meetings. The intern will have the opportunity to practice scientific communication during these meetings.
The faculties of management and computer science, to which the supervisor belongs, organize scientific communication events during the summer specifically dedicated to undergraduate research. The intern is encouraged to participate and will be supported in preparing presentation materials.

Skills required:
Excellent written English comprehension is required to be able to properly understand and code the interview transcript. French written comprehension is a bonus and would allow the intern to code French language transcripts.
Depending on the intern's spoken English or French skills, they will be encouraged to take part in the knowledge dissemination activities.
No prior knowledge of qualitative methods, change management literature, or scientific communication is required; we will provide training.

Research area: Case study on digital transformation including smart (data analytics and AI) for green transition

Student roles:
The student will be responsible for the literature review, data analysis and collaboration in preparing a synthesis and a report on the case study. The student will work in partnership with a PhD student, both of whom are under my supervision.

Skills required:
analytical skills, rigour, open-mindedness for team discussions, teamwork and autonomy

Research area: Agricultural and Resource Economics

Student roles:
The intern will take primary responsibility for assembling and analyzing the beef trade network. Specific tasks include compiling and cleaning bilateral trade data from UN Comtrade and FAOSTAT, harmonizing units to carcass-weight equivalent, and constructing the network in Python or R. The intern will compute standard network metrics across years, generate visualizations, and document methodological choices in a short technical memo. Weekly meetings with the supervisor will guide progress and interpretation. The intern will contribute to a final summary report and maintain a clean, reproducible code repository throughout the project to ensure that all analyses can be readily extended in subsequent work.

Skills required:
The intern should be an undergraduate in economics or a related field.
Required: proficiency in at least one scripting language (Python or R), familiarity with introductory statistics, and comfort working with tabular data.
Prior exposure to network analysis is welcome but not required; the relevant methods will be taught during the internship. The successful candidate will be detail-oriented, capable of independent problem-solving, and interested in food systems, trade, or environmental sustainability.
English and an ability to document code and analytical decisions clearly are essential to support reproducibility and effective team communication.

Research area: Dr. Nita Chhinzer (MBA, PhD) is an Associate Professor of Human Resources (Department of Management, University of Guelph). Her research is concentrated on Strategic Human Resources Management, with a strong focus on downsizing practices, procedures and outcomes. The proposed project is an extension of this program of research, plus a large focus on support on the data collection and updates to her Intro to Canadian Human Resources Textbook. To learn more see www.nitachhinzer.com

Student roles:
The request for 2 interns is due to the volume of work to be completed, and the desire to create a collaborative learning environment.
The researchers will help organize data and test the research hypotheses associated with existing literature and management theories, interpret results with Dr. Chhinzer, and assist with manuscript formatting based on a target journal’s guidelines (mostly APA or Harvard styles). The student will also be involved with reviewing associated layoff related literature and manuscripts to develop a stronger subject matter expertise on organizational downsizing (antecedents and outcomes).
They will also help find new stats (with guidance from Dr. Chhinzer) and review multiple chapters of the textbook, helping to improve the student experience as readers.

Skills required:
Eligible students must have
- Education in management, business, human resource management, industrial/organizational psychology or a related discipline.
- English language proficiency, including the ability to read and write in English effectively.
- An understanding of management theory is desirable, including an interest in organizational change management and decision making.
- Excellent time management skills
- Attention to detail

Research area: My research agenda focuses on three aspects of applied microeconomics and quantitative techniques, especially in relation to the environment and development. These are (a) Energy Use in Agriculture and Environmental Impacts, (b) Sustainable Tourism, and (c) Food policy, nutrition and livelihood. My most recent project has been on alleviating poverty and malnutrition in three biodiversity hotspot regions in India.

Student roles:
Review literature, collect secondary and primary data, organize and input data to MS Excel. Analyze, interpret and write reports/papers.
Create a database for analysis, maintain bibliography, conduct economic analysis, model building.
Annotate papers
Maintain database of secondary and online sources of data

Skills required:
The intern must have excellent analytical, oral and writing skills. Must be well versed in Excel and basic econometric analysis.
Have knowledge about circular economy and sustainable development goals.
Have knowledge of food and nutrition security from a social and economics perspective.
Have an understanding of the hospitality industry, food waste and supply chain analysis.

Research area: My research focuses on responsible artificial intelligence adoption, digital transformation, and organizational trust. I examine how employees, managers, and organizations evaluate the use of emerging technologies in the workplace, especially generative AI. This project studies how disclosing AI assistance affects perceptions of employee competence, trustworthiness, originality, motivation, ethicality, and performance across different work tasks and managerial evaluation contexts. It contributes to research on responsible AI use, workplace technology governance, human resource management, and organizational behavior.

Student roles:
The student will support the design and development of a vignette-based survey experiment on AI disclosure in workplace evaluations, with the goal of contributing to a journal manuscript. Their role will include reviewing academic literature on AI disclosure, social evaluation, trust, competence, motivation, employee performance, and workplace technology use. The student will help identify relevant theories and organize key findings into a literature review table.

The student will also assist in designing experimental scenarios where participants evaluate employees who either disclose or do not disclose AI assistance across different types of work outputs, such as writing, analysis, coding, decision support, and client communication. They may help refine survey questions, prepare experimental conditions, create measures for perceived competence and trust, and support the preparation of survey materials in Qualtrics or a similar platform.

Depending on project timing and ethics approval, the student may assist with pilot testing, data organization, cleaning survey data, coding variables, conducting basic descriptive analysis, and preparing tables or figures. The student will meet regularly with the supervisor to discuss research design, theory development, interpretation of findings, and manuscript development.

By the end of the internship, the student is expected to contribute to a literature summary, experimental design document, draft survey instrument, preliminary analysis materials, and sections of a draft manuscript. The overall aim is to advance the project toward submission to a peer-reviewed journal in the areas of organizational behavior, human resource management, information systems, or responsible AI governance.

Skills required:
The student should have an interest in management, psychology, organizational behavior, human resource management, information systems, business analytics, or research methods. Experience with survey research, experimental design, SPSS, Excel, Qualtrics, or basic statistical analysis is helpful but not required. The student should have strong writing skills, attention to detail, curiosity about generative AI in the workplace, and willingness to learn how to design and analyze vignette-based experiments.

Research area: This research focuses on social network analytics, artificial intelligence, and organizational behavior. It examines how informal relationships, communication patterns, and influence structures shape decision-making within organizations. The work integrates network analysis, natural language processing, and business analytics to uncover hidden structures that are not visible in formal organizational charts. The goal is to develop practical tools that help organizations better understand collaboration, influence, and information flow.

Student roles:
The student will design and implement methods to construct and analyze organizational networks. They will collect or generate datasets representing interactions between individuals, and preprocess these data for analysis.

The student will apply network analysis techniques to identify key structural features such as centrality, clustering, and connectivity. They will also integrate natural language processing methods to analyze communication content and link it to network structures.

Responsibilities include developing visualizations, interpreting results, and translating findings into practical insights. The student will contribute to building a prototype tool or framework that can be used by organizations to understand informal influence.

The student will document workflows, support reproducibility, and contribute to a final report. Regular meetings will support progress and interpretation. The project combines technical implementation with applied organizational analysis.

Skills required:
Students should have a background in business analytics, data science, sociology, or computer science. Experience with Python or R is required. Familiarity with network analysis, data visualization, or text analysis is an asset. Strong analytical thinking and interest in organizational behavior are important.

Research area: My research focuses on the development and application of quantitative and analytical methods to support decision-making in complex systems. I work in the areas of optimization, decision theory, MCDM, inventory modelling, time series analysis, fuzzy set theory, machine learning, and supply chain analytics. My research aims to develop models and decision support that help organizations improve efficiency and operational performance under uncertainty. By integrating operations research, artificial intelligence, forecasting techniques, and uncertainty modelling, I seek to address real-world challenges in supply chain management, logistics, healthcare operations, and business decision-making while bridging the gap between theory and practice.

Student roles:
The student will be actively involved in all phases of the research process, from problem identification to the dissemination of results. Responsibilities will include conducting comprehensive literature reviews to identify research gaps and emerging trends, collecting, cleaning, and analyzing quantitative and qualitative data, and developing mathematical, statistical, and computational models to address complex decision-making problems. The student will assist in designing and implementing optimization models, machine learning algorithms, forecasting techniques, inventory management models, and multi-criteria decision-making frameworks under both deterministic and uncertain environments. The role will also involve performing computational experiments, sensitivity analyses, scenario evaluations, and model validation using real-world datasets.
In addition, the student will contribute to the development of innovative methodologies by integrating techniques from operations research, artificial intelligence and data analytics. The student will be expected to interpret research findings, generate managerial insights, and translate technical results into practical recommendations for decision-makers. Responsibilities will also include preparing research reports and presenting findings at research group meetings. The successful candidate should demonstrate strong analytical and problem-solving abilities, intellectual curiosity, initiative, and the capacity to work both independently and collaboratively within an interdisciplinary research environment. Through this role, the student will gain valuable experience in advanced analytical methods, academic research, scientific writing, and industry-focused problem-solving while contributing to impactful research in supply chain management, logistics, healthcare operations, sustainability, and business decision-making.

Skills required:
The student should have a strong background in quantitative methods, mathematics, statistics, operations research, supply chain management, business analytics or a related discipline. Experience in optimization techniques, decision analysis, data analytics, and ML would be highly beneficial. The student should possess analytical and problem-solving skills, be comfortable working with mathematical models and large datasets, and have programming experience. Knowledge of statistical analysis, artificial intelligence, and fuzzy set theory is an asset. The successful candidate should also demonstrate strong communication skills, intellectual curiosity, independence, and a willingness to engage in interdisciplinary research that addresses challenges in SCM and business operations.

Research area: This project is in cybersecure AgFoodTech, technology adoption, innovation strategy, knowledge mobilization, and responsible AI. The research focuses on how farmers, agri-food SMEs, technology vendors, insurers, food processors, and government stakeholders understand and adopt secure digital agriculture technologies. It examines barriers to trust, cybersecurity awareness, perceived value, risk communication, and market readiness for AI-enabled agri-food systems. The project contributes to equitable and reliable AgFoodTech by developing stakeholder-informed strategies for communicating, adopting, and scaling cybersecure agricultural technologies.

Student roles:
The intern will work under supervision to study how agri-food stakeholders understand, evaluate, and adopt cybersecure AI-enabled technologies. The student will begin by reviewing selected literature and public reports on digital agriculture, cybersecurity awareness, responsible AI, innovation adoption, market readiness, and knowledge mobilization. Based on this review, the intern will identify key stakeholder groups and analyze their needs, concerns, incentives, and adoption barriers.

The intern will develop stakeholder personas for groups such as farmers, agri-food SMEs, co-operatives, food processors, technology vendors, insurers, and government or policy organizations. For each group, the student will examine what cybersecurity and AI risks matter most, what language or value propositions may resonate, and what barriers may slow adoption. The intern may also design a short survey or interview guide for future stakeholder engagement, although actual human-subject data collection is not required for this summer project.

The student will produce practical research outputs, including an adoption-barrier map, cybersecurity value-proposition matrix, communication recommendations, and a final market-readiness or knowledge-mobilization brief. The intern will meet regularly with the supervisor and research team, present progress, and contribute to a final presentation suitable for the SECURE-AGRO research training environment.

Skills required:
The ideal student will have a background in business, marketing, management, entrepreneurship, communications, economics, public policy, agri-business, or a related field. Programming experience is not required. The student should be comfortable reviewing literature, analyzing stakeholders, summarizing market and adoption barriers, developing clear written materials, and preparing presentation-style outputs. Interest in agriculture, food systems, cybersecurity, responsible AI, innovation adoption, or technology commercialization would be highly valuable. Strong communication, organization, and analytical skills are important.

Research area: I study how organizations adapt to technological innovation, focusing on how innovation is enacted within practices and reflected in strategic discourse. My research program develops across two complementary streams. First, I examine how innovations are adopted, resisted, and embedded within organizational practices across hierarchical levels of actors (from senior leadership to frontline employees), shaping transformation outcomes. Second, I analyze how organizational culture of innovation is expressed in firm communications, using large language models (LLMs) to detect early signals of innovation orientation in CEO letters and their link to performance. Together, these streams provide a multi-level perspective on innovation in organizations.

Student roles:
The student will work with me on several projects examining how organizations adopt and implement technological innovation. This might include the following:
(1) Performing literature reviews on topics such as organizational innovation, practice theories, LLM bias
(2) Performing data cleaning and analysis
(3) Using python or R to create prompt for LLM data analysis

Personal traits and qualities I look for in students
(1) Openness to admitting when you have made a mistake or are struggling - the faster you tell me, the faster we can fix it together.
(2) Passion for research - you love asking questions and are a very curious person
(3) Positive response to feedback - you can take constructive criticism and use it to grow, rather than letting it get you down
(4) Autonomous worker - You can organize your time and solve problems. You know when to reach out for help to stop you from getting stuck.

In general, I tend to conduct ethnographic studies and be very hands-on in the summers. I treat summer research as a focused and productive period of work. Students should expect to be fully engaged and to make substantial progress over the course of the internship. The student may have the opportunity to work with other summer students and perhaps PhD students as well.

They should expect to meet with me in-person at least once a week for check-ins on progress. My goal is to help students develop skills in the areas they're passionate about, so I am happy to adapt the assigned research tasks to the skills you would like to learn. My goal is always to provide you with the support you need without micromanaging.

Skills required:
REQUIRED TECHNICAL CAPABILITIES
• The student must have taken at least one qualitative data analysis course, and must be comfortable interpreting qualitative data such as interview transcripts and field notes
• The student must have taken at least one statistics course, and must be comfortable interpreting statistical tests
• The student's level of English is high enough that they are able to read and understand academic papers in marketing across literature streams
NICE-TO-HAVE TECHNICAL CAPABILITIES
• Familiarity with a coding language like R or Python -- please still apply if you do not have this, but are willing to learn.

Research area: My research examines how digital technologies and AI shape individual and societal outcomes, with a focus on sustainability, behavioral change, and the broader implications of intelligent systems. I use computational methods including agent-based modeling, NLP, and topic modeling to study how AI-driven technologies influence decision-making across multiple levels of analysis. As an assistant professor of Information Technology Management at Toronto Metropolitan University, my work spans Green IS, generative AI, and technology governance. My current research investigates how AI-related environmental risks are disclosed and framed in corporate reporting, using longitudinal computational text analysis to assess transparency and accountability in AI development.

Student roles:
The student will play a central role across all stages of the research pipeline, working closely with the supervisor from corpus construction through to paper writing. The position combines technical NLP work with applied policy-relevant research, giving the student experience in both computational methods and academic knowledge production.
1- In the first phase, the student will retrieve 10-K filings from the SEC EDGAR database, extract the Risk Factors and environmental disclosure sections, and build a clean, well-documented corpus. This includes developing and refining the keyword and terminology strategy used to identify AI-related environmental content across firms and years.
In the second phase, the student will collaborate with the supervisor to build a domain-specific lexicon covering AI infrastructure, energy consumption, carbon intensity, and sustainability language. The student will conduct exploratory keyword frequency analysis and apply BERTopic and LDA topic modeling to identify dominant risk clusters and track how they evolve over the 2015 to 2025 period.
3- In the third phase, the student will develop and validate a transparency scoring rubric through collaborative human coding and inter-rater reliability testing. This phase trains the student to bridge qualitative judgment and quantitative measurement, a skill that is increasingly valued in both IS research and data science practice.
4- In the fourth phase, the student will conduct comparative industry analysis and temporal trend analysis, examine the gap between risk disclosure intensity and mitigation specificity, and produce visualizations summarizing the findings across firms and time periods.
Throughout the internship, the student will participate in weekly research meetings, maintain clean and reproducible code, and contribute to writing the methods and results sections of a research paper.

Skills required:
The ideal candidate has strong Python programming skills and hands-on experience with natural language processing, including text preprocessing, topic modeling, and document classification. Familiarity with libraries such as gensim, spaCy, HuggingFace Transformers, or BERTopic is an asset. The student should be comfortable working with large unstructured text datasets and understand standard machine learning evaluation metrics. A background in computer science, data science, or a related field is expected. Experience with financial documents or regulatory filings is not required. Interest in sustainability, corporate governance, or AI policy is an advantage and will help motivate engagement with the rproject.

Research area: My research focuses on responsible artificial intelligence adoption, digital transformation, and workplace technology governance. I study how employees, managers, and organizations respond to emerging technologies such as generative AI, particularly in relation to trust, productivity, ethics, cybersecurity awareness, and organizational policy. This project examines the human and organizational side of AI use at work, including why employees may hide, disclose, or selectively reveal AI assistance.

Student roles:
The student will support the development of the qualitative study. Their role may include reviewing academic literature on AI use, workplace disclosure, impression management, trust, and technology adoption; helping refine interview questions; assisting with recruitment materials; organizing interview data; supporting thematic coding; summarizing findings; and preparing research notes, tables, and short reports.

The student will meet regularly with the supervisor to discuss research progress and receive guidance on qualitative research design and analysis. Depending on ethics approval and project timing, the student may assist with interview preparation, transcription review, coding, and interpretation of themes. The student will also help identify practical implications for managers and organizations seeking to develop responsible AI-use policies.

By the end of the internship, the student is expected to contribute to a literature summary, coded data structure or thematic map, and a short research report that can support a future journal manuscript.

Skills required:
The student should have an interest in organizational behavior, psychology, human resource management, management, information systems, or digital technology adoption. Experience with literature reviews, qualitative research, interviews, coding interview data, or thematic analysis is helpful but not required. Strong writing, critical thinking, attention to detail, and willingness to learn research methods are important.

Research area: Dr. Wu's expertise is in Industrial and Retail Marketing strategies, including Consumer Strategic Decision, Pricing, Supply Chain/Channel Relationship Management, Advertising Strategy, and emerging E-Commerce business models. His research uses game-theoretical models and advanced empirical models to explore firms' and consumers' strategic behaviours. His recent research projects include communication in joint consumption, influencer marketing, AI adoption and brand visibiilty in AI search, target advertising, and pricing and management strategies of E-commerce platforms.

Student roles:
The research intern will work closely with Dr. Ruhai Wu and doctoral students on a project examining household adoption decisions for healthcare technologies. The intern will participate in several stages of the research process, including:
1. Literature Review and Evidence Collection: Conduct reviews of academic literature and industry reports related to healthcare technology adoption, consumer decision-making, digital health, aging in place, and artificial intelligence applications in healthcare.
2. Data Management and Processing: Assist with organizing, cleaning, and integrating survey, operational, and secondary datasets. Responsibilities may include data validation, handling missing values, constructing variables, merging datasets from multiple sources, and preparing datasets for statistical analysis.
3. Quantitative Data Analysis: Support empirical analysis using statistical software such as R, Stata, SPSS, or Python. Tasks may include descriptive analysis, regression modeling, data visualization, robustness testing, and interpretation of findings. Students with stronger quantitative backgrounds may have opportunities to participate in advanced econometric analyses.
4. Research Communication and Knowledge Mobilization: Assist in preparing research reports, presentations, and summaries for academic, industry, and healthcare audiences. The student will help translate research findings into actionable insights for healthcare providers, technology developers, and policymakers.
5. Research Training and Collaboration: Participate in regular meetings with faculty members and graduate students, gaining exposure to the design, execution, and dissemination of interdisciplinary research in marketing, economics, and healthcare.

Students who make substantial intellectual contributions to the project may be recognized through co-authorship on conference papers or academic publications, consistent with standard academic practice.

Skills required:
Applicants should have a strong interest in quantitative research in marketing, economics, healthcare, or related fields. Candidates are expected to have completed foundational coursework in microeconomics and econometrics. Advanced training in econometrics, statistics, data analytics, database management, or game theory will be considered an asset. Applicants should be familiar with data management and analysis using at least one statistical or programming software package, such as R, Stata, SPSS, or Python. Preference will be given to students with prior experience in empirical research, data analysis, survey research research projects. Strong analytical, problem-solving, written and oral communication skills are essential.

Research area: Dr. Tate Cao’s research specializes in engineering entrepreneurship education, innovation ecosystems, and technology commercialization, with a focus on how academic programs can effectively develop entrepreneurial mindset and practice among engineering students. His work integrates program design, curricular and co-curricular alignment, and experiential learning models, particularly within deep-tech and applied innovation contexts. He also examines assessment frameworks for entrepreneurship education, including both traditional venture outcomes and broader, long-term impacts such as career trajectories and innovation capacity. Complementing this, his applied research spans industry-collaborative innovation projects in areas such as ag-tech, biomedical engineering, and digital systems, linking education with real-world commercialization pathways.

Student roles:
Students will contribute to the development of an evidence-based framework for measuring entrepreneurship education impact. Key responsibilities include:

Literature Review and Benchmarking
Students will conduct structured reviews of academic literature, institutional reports, and existing evaluation frameworks. They will identify commonly used metrics and approaches to assessing entrepreneurship outcomes across universities and organizations.

Data Collection and Organization
Students will collect and organize data on how institutions measure outcomes such as venture creation, funding, employment, and alumni success. They will build datasets or structured summaries to support comparative analysis.

Framework Development Support
Students will assist in defining categories of impact (short-term vs. long-term, quantitative vs. qualitative). They will help refine metrics and suggest methods for capturing less tangible outcomes such as entrepreneurial mindset and innovation capacity.

Survey and Tool Design
Students may contribute to drafting sample survey questions or assessment tools aimed at capturing student and alumni outcomes. This may include designing instruments for longitudinal tracking.

Analysis and Synthesis
Students will help synthesize findings into coherent insights, identifying gaps in current measurement approaches and proposing improvements. They will contribute to drafting sections of the final report and creating visual representations of the framework.

Communication of Results
Students will assist in preparing presentations and summary documents for stakeholders. They will participate in discussions on how to implement the framework in a real academic setting.

This experience will provide students with practical skills in impact evaluation, data analysis, and research design applied to entrepreneurship education.

Skills required:
Students with interests in entrepreneurship, data analysis, education research, or social science methodologies are encouraged to apply. Backgrounds in engineering, business, economics, statistics, or public policy are welcome. Strong analytical thinking and attention to detail are essential. Experience with data collection, survey design, or statistical analysis is beneficial but not required. Students should be comfortable working with both qualitative and quantitative data and have strong written communication skills. Familiarity with entrepreneurship ecosystems or impact assessment frameworks will be considered an asset.