Science and Technology

Research area: My research focuses on artificial intelligence, optimization, quantum computing, and decision-support systems for industrial decarbonization and energy transition. We develop advanced computational tools that integrate machine learning, forecasting, uncertainty analysis, and quantum-inspired optimization methods to support strategic decision-making in complex industrial systems. Our work helps organizations evaluate emerging technologies, manage risks, and identify optimal pathways toward sustainable and low-carbon futures.

Student roles:
The student will contribute to the development of forecasting, optimization, and decision-support models for evaluating decarbonization investments. Responsibilities include data analysis, implementation of machine learning algorithms, uncertainty assessment, and experimentation with quantum-inspired optimization techniques using platforms such as Qiskit. The student will compare classical and quantum-enhanced approaches for technology selection and portfolio optimization and contribute to technical reports, presentations, and scientific publications.

Skills required:
Students should have a background in engineering, computer science, data science, operations research, applied mathematics, economics, or related disciplines. Knowledge of programming (Python preferred), machine learning, optimization, statistics, or data analytics is desirable. Familiarity with energy systems, sustainability assessment, techno-economic analysis, or decision-support methods is an asset. Exposure to quantum computing concepts, quantum optimization, or platforms such as Qiskit is beneficial but not required. Students interested in applying AI and emerging quantum technologies to support sustainable investment decisions are encouraged to apply.

Research area: My research focuses on artificial intelligence, advanced process control, reinforcement learning, and decision-support systems for industrial operations. We develop intelligent supervisory control architectures capable of coordinating multiple process units, optimizing plant-wide performance, and enhancing operational resilience. Our work combines machine learning, control engineering, digital twins, and optimization techniques to improve productivity, energy efficiency, and sustainability in energy-intensive industries.

Student roles:
The student will assist in developing and evaluating AI-based supervisory control algorithms for industrial systems. Responsibilities include data analysis, implementation of reinforcement learning and optimization methods, simulation studies using digital twins, performance assessment, and visualization of results. The student will collaborate with researchers in AI and control engineering and contribute to technical reports, presentations, and scientific publications.

Skills required:
Students should have a background in electrical engineering, computer engineering, control systems, computer science, applied mathematics, or related disciplines. Knowledge of programming (Python preferred), machine learning, control theory, optimization, or reinforcement learning is desirable. Familiarity with PID controllers, dynamic systems, or process simulation is beneficial but not required. Students interested in AI-driven automation and industrial innovation are encouraged to apply.

Research area: Professor Karin Kleiner focuses on advanced lithium-ion battery materials, combining synthesis, electrochemistry, and operando characterization to understand performance and degradation mechanisms. She develops new materials to improve sustainability, energy density, and cost efficiency for next-generation lithium-ion batteries. Her group also investigates direct and hydrometallurgical recycling of cathode materials to enable a low-carbon circular battery economy.

Student roles:
The student will play a central role in developing and applying a spectroscopy-informed, DFT-based computational framework for the discovery of advanced energy materials.

A key responsibility is to analyze how composition, bonding, and redox processes influence material performance. This includes studying σ- and π-bonding interactions, tracking electronic structure evolution during charge compensation, and interpreting computational results in relation to core-level spectroscopic data. The student will contribute to building physically grounded structure–property relationships that link atomic-scale chemistry to macroscopic electrochemical behavior.

The role also includes high-throughput exploration of compositional space, including Ni–Co–Mn variations and targeted dopants or substitutions, to identify trends in stability, voltage behavior, and cycling performance. The student will investigate key degradation mechanisms such as oxygen redox reversibility, voltage fade, and cation migration, particularly in Li-rich cathode materials.

The position involves data analysis, scientific visualization, participation in group discussions, and contribution to reports and publications within an interdisciplinary and collaborative research environment.

Skills required:
The student should have a background in materials science, chemistry, physics, or a related field, with a solid understanding of solid-state chemistry and electrochemistry. Knowledge of transition metal oxides, Li-ion battery cathode materials, and electronic structure concepts (bonding, band structure, redox processes) is important. Experience with density functional theory (DFT), atomistic modeling, or computational materials science is a strong advantage, along with basic programming skills (e.g., Python) and data analysis. Familiarity with spectroscopic techniques such as XPS or XAS is beneficial but not required. The student should be motivated, analytical, and able to work in an interdisciplinary research environment.

Research area: My research focuses on sustainable biomass conversion and biorefinery technologies to produce renewable fuels, chemicals, and materials from agricultural and forestry residues. A major emphasis is the development of greener solvent systems, including deep eutectic solvents and other bio-based solvents, for biomass fractionation and valorization. The research integrates biomass chemistry, green chemistry, process design, and materials development to improve the efficiency, sustainability, and environmental performance of biomass utilization. The overall goal is to support the transition toward a circular bioeconomy by converting underutilized biomass resources into value-added products.

Student roles:
The student will support the development and evaluation of a green solvent-assisted biomass fractionation process. Their role will include assisting with literature review, experimental planning, biomass sample preparation, solvent preparation, fractionation experiments, sample recovery, and basic characterization of biomass-derived fractions. The student will work under the supervision of the principal investigator and senior lab members and will receive training in relevant laboratory procedures and safety protocols.

Specific tasks may include drying and milling biomass feedstocks, preparing green solvent mixtures, conducting small-scale fractionation experiments, separating solid and liquid fractions, washing and drying samples, and recording mass balances. The student may also assist with analytical measurements to evaluate process performance, such as determining solid recovery, lignin removal, cellulose enrichment, or enzymatic digestibility. Depending on the student’s skills and the progress of the project, they may also help analyze characterization data and compare the effectiveness of different solvent systems or processing conditions.

The student will be expected to maintain a well-organized lab notebook, follow safety guidelines, communicate progress regularly, and participate in lab meetings or discussions. By the end of the internship, the student is expected to summarize their findings in a short report or presentation. Through this role, the student will gain research experience in biomass conversion, green solvent chemistry, and sustainable biorefinery development.

Skills required:
The student should have a background in chemistry, chemical engineering, materials science, environmental science, bioresource engineering, or a related field. Basic laboratory experience, careful experimental technique, and an interest in green chemistry, biomass utilization, or sustainable materials are desirable. Familiarity with biomass composition, solvent systems, analytical chemistry, or data analysis would be helpful but is not required. The student should be motivated, responsible, willing to learn new techniques, and able to work both independently and as part of a research team.

Research area: Nanotechnology, or more appropriately nanoscience, is a multidisciplinary branch of science that currently receives a lot of attention from researchers in the pharmaceutical and biomedical fields. Using nanoparticles in drug delivery and diagnostics offers several advantages over traditional formulations, such as modified pharmacokinetics and tissue distribution, site-specific targeting, reduced toxicity, prolonged release, improved stability and bioavailability. My research program aims at understanding the interaction of nanoparticles within the different biological compartments and translating this knowledge to design new generations of bio-inspired nanoparticles for applications in cancer therapy and fetal targeting.

Student roles:
After appropriate biosafety training and other required training at the College of Pharmacy, the students will be trained on various techniques in the lab including nanoparticle synthesis and characterization, microfluidics and cell culture techniques. The student will be mentored by Dr. Labouta directly and also work closely with a postdoc and a graduate student. We encourage students to present their results at weekly lab meetings, and participate in the summer poster competition and local/national conferences.

Skills required:
A Master's or a Bachelor student in life sciences, pharmacy, biomedical engineering, mechanical engineering or a similar field. Cell culture/fluid flow simulation experience is preferred. Experience with nanoparticles is an advantage but not required. Prior evidence of a pervious research experience is a plus.

Research area: My research area is in neurorehabilitation. My studies range from the study of basic sensorimotor integration mechanisms in the control of posture and balance, to the clinical application of new tools and technologies for the assessment and intervention of balance and mobility disorders.

Student roles:
Student will assist in the development of new virtual environments incorporating biofeedback, to evaluate and improve balance and mobility control.

Skills required:
Students should be comfortable with motion capture and analysis, and grasp the fundamentals of biofeedback. The ability to code or program in Unity Pro or Unreal will be an asset but not absolutely required.

Research area: Nanotechnology, or more appropriately nanoscience, is a multidisciplinary branch of science that currently receives a lot of attention from researchers in the pharmaceutical and biomedical fields. Using nanoparticles in drug delivery and diagnostics offers several advantages over traditional formulations, such as modified pharmacokinetics and tissue distribution, site-specific targeting, reduced toxicity, prolonged release, improved stability and bioavailability. My research program aims at understanding the interaction of nanoparticles within the different biological compartments and translating this knowledge to design new generations of bio-inspired nanoparticles for applications in cancer therapy and fetal targeting.

Student roles:
The student will be involved in a cutting edge research program to design various nanomaterials/nanoparticles with different size, composition and surface properties. The student will then test these particles using in vitro cell models developed in the lab. The student will work in a collaborative environment among a team of researchers with different backgrounds who will guide him/her in his summer research project. The student will also attend lab meetings and will present his work to the team. Depending on the student's progress and contribution to the project, he or she can be an co-author on the outcome publication.

Skills required:
A Master's or a Bachelor student in chemistry, life sciences, pharmacy, biomedical engineering or a similar field. Cell culture experience is preferred. Experience with nanoparticles or lipid/polymer chemistry is a plus.

Research area: Statistical Modelling, Data Analysis, Sample Surveys, Design of Experiments

Student roles:
Data collection from published resources, Study provided literature on statistical models, Analyze data and Prepare report

Skills required:
Basic Statistics, Any statistical software

Research area: In the field of thin films and nanotechnology, I am engaged in the research of transparent photovoltaic cells. These cells exploit the photovoltaic effect in semiconductor materials, allowing the passage of light while simultaneously generating electrical energy. At the same time, I explore the field of chromogenic materials for smart windows, which have the ability to dynamically adjust their optical properties in response to external stimuli. Embracing advanced thin film technologies and remarkable characteristics of materials including nanomaterials, I strive to unleash the potential of these advanced technologies.

Student roles:
The student will participate in the synthesis and characterization of Fe-doped Cu₂O thin films prepared via the sol-gel method. He/She will be responsible for solution preparation, thin-film deposition using spin-coating, and thermal treatment of the samples. The student will also perform optical (UV-Vis), electrical (I–V curves, conductivity), and structural (XRD, SEM if available) characterizations. He/She will analyze experimental data to evaluate the effect of doping on material properties. In addition, the student will contribute to the fabrication and testing of simple solar cell devices based on the developed materials and assist in preparing technical reports and presenting project results.

Skills required:
The student should be enrolled in the fourth year of an undergraduate program in physics, engineering physics, materials science, or a related field. A solid understanding of basic concepts in semiconductors, optics, and electronics is required. Prior laboratory experience, including solution preparation, material handling, or thin-film deposition techniques (e.g., spin-coating), is considered an asset. Familiarity with characterization methods (UV-Vis spectroscopy, electrical measurements, XRD) is beneficial but not mandatory. The student should demonstrate autonomy, rigor, analytical thinking, and the ability to work effectively in a team.

Research area: In the field of thin films and nanotechnology, I am engaged in the research of transparent photovoltaic cells. These cells exploit the photovoltaic effect in semiconductor materials, allowing the passage of light while simultaneously generating electrical energy. At the same time, I explore the field of chromogenic materials for smart windows, which have the ability to dynamically adjust their optical properties in response to external stimuli. Embracing advanced thin film technologies and remarkable characteristics of materials including nanomaterials, I strive to unleash the potential of these advanced technologies.

Student roles:
The student will participate in the synthesis and characterization of NiO thin films prepared via the sol-gel method. He/She will be responsible for solution preparation, thin-film deposition using spin-coating, and thermal treatment of the samples. The student will also perform optical (UV-Vis), electrical (I–V measurements, conductivity), and structural (XRD, SEM if available) characterizations. He/She will analyze experimental data to evaluate the effect of doping on material properties. In addition, the student will contribute to the integration of the films into perovskite solar cell devices and assist in preparing reports and presenting research results.

Skills required:
The student should be enrolled in the fourth year of an undergraduate program in physics, physics engineering, materials science, renewable energy or a related field. A solid understanding of basic concepts in semiconductors, optics, and electronics is required. Laboratory experience is an asset, particularly in solution preparation, chemical handling, and thin-film deposition techniques (e.g., spin-coating). Familiarity with material characterization methods (UV-Vis spectroscopy, electrical measurements, X-ray diffraction) is desirable but not mandatory. The student should demonstrate autonomy, scientific rigor, strong analytical skills, and the ability to work in a team.