This project will aim to use the output from high to very high resolution numerical models (structured and unstructured) to examine past changes in the ocean and sea-ice in the regions around Canada (Canadian Arctic Archipelago, Baffin Bay and the northern Labrador Sea), examining key underlying physical and dynamical processes associated with these changes. The long term goal will be to work towards understanding how the waters and sea-ice around Canada will evolve in the future, and to provide that information to the many stakeholders concerned with such information. Historical data analysis (section as well as satellite data) will be used to evaluate model performance. The student will use model output to examine key scientific questions. Exact projects will depend on the background and experience of the student. Previous projects have included analysis of cascading of dense waters from the shelf to the deep ocean; analysis of ocean transports along the coast of Greenland and examining processes that drive air-sea gas exchange in the central Labrador Sea.
Research area, student roles & skills
Research area: My research uses high to very high resolution ocean general circulation models to study the ocean's role in the climate system. Question very from how to best represent key processes in ocean and sea-ice models to using those models to answer questions of climate variability and impacts. My research focussed on the high latitudes and the Arctic and North Atlantic Oceans. My research also involves ocean cyrospheric links, such as how warm ocean waters impact tide water glaciers in Greenland, and then how that melting impacts the ocean circulation, as well as the ocean's biogeochemistry.
Student roles: The student will be given a specific research project, or sub-project related to issues of ocean data and model analysis. The student will be required to analyze and visualize the given data using matlab. Tasks may be specific as visualization data from a given section, to as general as analyzing the transport across several geographic domains using several different approaches and reporting back on the strengths and weaknesses of each approach, including comparisons of the calculations with other estimates in the scientific literature. Specifics will depend on the skills of the given student.
Skills required: A strong math, physics, engineering or computer science background is needed. Ability to work in linux based environment. Familiarity with writing analysis and visualization scripts in matlab. A desire to be a member of a larger research team and interact with researcher involved in different projects to learn about the oceans and our environment. Strong verbal communication skills. Self-Motivated. Familiarity with the oceans, atmosphere or climate would be nice, but is not necessary.
To apply advanced AI modelling systems to disentangle the effects of environmental attributes (trees, species, age,...) and human interventions (management strategies, land use) on forest ecosystems and their servcies and associated risks incl. e.g. fire, tree mortality, carbon, global change...
Research area, student roles & skills
Research area: I am specialized in Forest System Modelling and Economy using BigData and Process Understanding. I also apply System Dynamics models for largescale simulations e.g. under fire risk, global change.
Student roles: Manage Dataset Outline the concept for the analysis develop and run AI models Synthesize outcomes write reports
Skills required: Programming in R or Python or other languages Good statistical competences good writing skills
3. Assessing the impacts of storm surge on coastal wetlands in PEI using remote sensing
Supervisor: Xander Wang
University: University of Prince Edward Island (Charlottetown campus)
Location: Charlottetown, Prince Edward Island
Start date: 2027-05-03 (flexible)
Disciplines: Atmospheric Science, Earth Science, Engg-Civil, Engg-Environmental, Engineering, Environmental Studies, Geography, Human Ecology, Public Policy and Administration
Wetlands in the coastal areas of Prince Edward Island (PEI) are important not only for providing habitat for fisheries and wildlife but also they act as a flood retention basin. Due to its geographical position in cyclone-prone areas, PEI’s coastal wetlands are affected by storm surge almost every year. The sediment deposition and erosion process carried out by the storm surge changes the wetlands’ morphology (depth, channels) and water quality, ultimately impacting the whole wetland habitat and the plants and wildlife that depend on it. Climate change may aggravate the situation as more severe cyclones and storm surges are anticipated in the future. Understanding the historical changes in coastal wetland habitat due to storm surges will help to determine what appropriate management measures could be taken to reduce the impacts on the wetlands. Currently, there is limited data and research available in this respect. This research aims to determine the changes in coastal wetlands (spatial extent, sedimentation, vegetation cover, channels, etc.) in PEI over the last decade. This study will analyze historical remote sensing images of some selected coastal wetlands to determine the changes. The results of this research will be published in a journal.
Research area, student roles & skills
Research area: My research specialization includes climate change impact assessment, flood risk assessment, climate change mitigation and adaptation, forest ecosystem services, and nature-based solutions to natural hazards.
Student roles: The student will be involved in the literature review, secondary data collection, remote sensing image processing, and mapping, field visits, data analysis, and report/journal article preparation.
Skills required: The student must have an understanding of the coastal environment including wetlands, and climate change-related hazards. He/she should have basic level skills in using ArcGIS and data processing software (e.g., Excel, R).
4. Characterizing heavy metal contamination in the atmosphere of Montreal (Canada) since 1968
Montreal is the second largest city in Canada with about 4.4 million inhabitants. While the city's overall air quality remains acceptable, local hotspots and industrial facilities frequently trigger regulatory action and public health concerns. The city actively tracks air pollutants through the Réseau de surveillance de la qualité de l’air (RSQA), which monitors for example airborne particulate matter (PM). The annual average for PM2.5 was 10 μg/m3 in 2019.
This project proposes to study monthly metal concentrations in aerosol samples collected from the city's various monitoring stations since 1968. This will enable us to draw up a portrait of pollution levels, their possible seasonal variations and the exposure of residents.
Research area, student roles & skills
Research area: I am a Professor at the Earth and Atmospheric Science Department at UQAM with more than 25 years of experience in isotope geochemistry. I have federated my research group around interdisciplinary environmental issues, mostly water/air/soil pollution. The striking contemporary nature of my research is appealing to a lot of students eager to develop creative knowledge that could concretely lead to a better environment. While most of my students are from UQAM, I have attracted a large number of international students: I recently supervised students originating from Canada, Tunisia, Poland, France, China, Ivory Coast, Mauritania, Nepal, Pakistan, Burkina Faso and Cuba.
Student roles: The enrolled student will be in charge of the chemical (metal concentrations) analysis of the samples. The student will be trained by our laboratory manager to prepare and analyze the samples in our clean labs at UQAM. The student will be in charge of generating the database and will take part into the interpretation of the dataset. Our objective is to publish a joint article after the internship that will be coauthored by the student.
During her/his stay the student will acquire cutting edge analytical expertise, benefiting from an access to analytical facilities unique in Eastern Canada. The laboratory training of my students is realized either by myself or by highly skilled lab managers. We supervise them in operating these instruments and assure QA/QC controls. Beyond generating trustable data, I want my students to develop a full autonomy in running our instruments. The analytical expertise my students acquire is obviously not limited to geochemistry but applicable to multiple environmental issues. The overall team spirit developed at all levels among my HQP is a key component for our scientific success as well as for developing collaborative reflexes they will use during their career.
Skills required: I am looking for a motivated student, ready to face new challenges. A background in Earth Sciences is a plus. Although experience in the laboratory is not mandatory, the selected student should be ready to work in a clean laboratory environment as well as to be able to run measurements on state-of-the art instrumentation for extensive periods. Abilities with database (e.g. Excel or similar), word processing (e.g. Word or similar) as well as data visualization and analysis softwares (e.g. Grapher or similar) are required. The project will be conducted either in English or French, depending on the student proficiency.
5. Data-driven modelling of stratified environmental flows
The aim of the project is to develop a new generation of models for turbulent mixing in natural waters, e.g. lakes, estuaries, and oceans, where the density of fluid typically increases with depth (i.e. stratified). Understanding stratified mixing is crucial for a range of environmental problems, from modelling the ecological health of the Great Lakes to predicting oceanic heat uptake in a changing climate.
The integration of scientific computing and data science is driving a paradigm shift in natural sciences and engineering. We will leverage a recent high-fidelity turbulent flow dataset and explore data-driven modelling strategies for stratified environmental flows. While the governing equations for fluid flows have been well known, solving them directly is prohibitively expensive for most practical applications. Instead of solving the equations, we look for an optimal dynamical system representation of complex flows based entirely on data, aiming at new physical insights on these flows that can be readily used by practitioners. To this end, we will employ proper orthogonal decomposition (POD) and dynamical mode decomposition (DMD) which are two powerful tools in the burgeoning fields of machine learning and compressed sensing. Both methods will be applied to existing flow data in order to construct a dynamical system representation of a stratified wake, a canonical turbulent free-shear flow. We will then develop reduced-order models for this flow, which will lead to insights on the underlying physics and reduce computational costs for engineering applications.
Research area, student roles & skills
Research area: Civil engineering; environmental fluid mechanics; numerical analysis; computational fluid dynamics; data analytics; machine learning
Student roles: The role is suitable for an engineering or physics student who wishes to gain experiences in computing. The student will work under the guidance of the professor and graduate students and receive training in Linux, Matlab and scientific visualization software, using high-performance computing resources provided by Compute Canada. The main duty is to explore dimensional reduction tools (POD & DMD), visualize qualitative features of dynamical modes extracted, and participate in discussions of flow physics.
Skills required: Strong interest in numerical analysis, physics and applied mathematics Some experience in programming (e.g. Matlab) Good communication skills and willingness to teamwork
6. Describing Pathways for Baffin Bay Storms
Supervisor: Alex Crawford
University: University of Manitoba (Winnipeg campus)
Baffin Bay, located between Greenland and the Canadian Arctic Archipelago, is a cul-de-sac for extratropical cyclones. Storms that enter Baffin Bay tend to come from the west or south, and then stall out and slowly dissipate within the Bay. Only rarely do they escape out over the Greenland Ice Sheet to the east or Ellesmere Island to the north. These storms can have big impacts on the accumulation of snow in Greenland and the eastern Canadian Arctic; they pose notable hazards to hikers and skiers in Auyuittuq National Park on Baffin Island; and they tend to disrupt the seasonal growth of sea ice within Baffin Bay, which in turn impacts migration of marine mammals, shipping for mining operations and resupply, and hunting and fishing by Baffin Bay communities. However, the year-to-year variation in storm activity is high. For example, winter 1942-43 saw only 30 storms, whereas the next winter (1943-44) saw 60 -- twice as many. this means some years are rather calm, while others are excessively stormy and can shut down whole communities (e.g., as recently happened in Clyde River, Nunavut, when snow removal equipment broke during the stormiest winter on record).
In this project, we want to better understand the variability of storm activity over Baffin Bay. This includes a few goals: 1) Better describe the different pathways that extratropical cyclones take into Baffin Bay using a clustering routine, 2) use composite analysis to detail the differences between storms that enter via these different pathways, 3) evaluate large-scale circulation patterns that govern the seasonal-scale variability in storm activity from these different pathways, and 4) exploring long-term changes to the pathways of storms into Baffin Bay and how those changes may relate to change sin the large-scale circulation patterns.
Research area, student roles & skills
Research area: My research area, broadly, is climate science, with an emphasis and mid- and high-latitudes. I have particular expertise in large storm systems called extratropical cyclones, their interactions with other parts of the Earth's system, and their response to long-term climate change. I use a combination of observational tools (weather stations, satellites) and physical modelling (weather models, climate models), and atmospheric reanalysis (which blends observational and modelling output).
Student roles: The role of the Mitacs student in this project will be to focus on the first two of four objectives. They will use a subset of a large extratropical cyclone database that I manage. They will apply a clustering routine, which other students working with me have developed, to define different clusters of storms that migrate into Baffin Bay. They will then perform composite analysis of storm characteristics for those different pathways based on data from the ERA5 and CARRA 2 atmospheric reanalyses. (For example, what is the intensity, the precipitation, the vertical structure, the longevity?) Finally, they will produce a series of figures (maps and graphs, which may be animated) with short, explanatory text for a general audience that will be hosted on a nascent website for research related to Baffin Bay and, specifically, Auyuiittuq National Park (on Baffin Island). The preliminary first page for this is located here: https://sites.google.com/view/climate-crawford/auyuittuq-national-park, and is being produced in consultation with Parks Canada staff.
This is a computer-based, analytical project that complements other work being conducted by myself, Dr. Karen Alley at University of Manitoba, and Dr. Luke Copland at the University of Ottawa. The student will interact with and share data with other team members, including other undergraduate and graduate students (the exact number depending on funding). There work will be included in any reports to Parks Canada (with the student as a co-author).
Skills required: Required Some background in atmospheric science (e.g., intro-level coursework) Computer programming experience (e.g., Python, R, MatLab) Enthusiasm for data science and weather/climate Organizational skills (e.g., be able to explain and reproduce methods used) Ability to think critically and work productively without needing assistance from AI
This project will aim to use historical data analysis, numerical model development (e.g. finite element models) and high resolution numerical modelling (structured and unstructured) to examine past changes in the ocean and sea-ice in the regions around Canada (Canadian Arctic Archipelago, Baffin Bay and the northern Labrador Sea), examining key underlying physical and dynamical processes associated with these changes. The long term goal will be to work towards understanding how the waters and sea-ice around Canada will evolve in the future, and to provide that information to the many stakeholders concerned with such information. Historical data analysis will use historical section data collected on Canadian and international cruises to compute geostrophic velocities and transports (volume, heat and freshwater) across the major boundary currents along Greenland and the east coast of Canada, as well as within the Canadian Arctic. Where available, more recent data collected by Argo floats and biological based sampling (e.g. seals) will be used and objectively mapped to the observational stations/sections used in the transport analysis, to focus on the seasonal cycle and seasonal variability (especially in winter when there are limited ship based observations in these regions).
Research area, student roles & skills
Research area: I would summarize my current research as focused on the role of freshwater in the oceans, as well as links between the Arctic and North Atlantic Oceans. This research involves a combination of the analysis of oceanographic data with numerical modeling as well as more theoretical ocean model development. Specific scientific questions are related to the impact of freshwater in these basins, explanations for observed variability at inter-annual and inter-decadal time scales as well as the linkages between these basins. My main geographical areas of research are the Canadian Arctic Archipelago, Baffin Bay, the sub-polar North Atlantic and Labrador Sea.
Student roles: The student will be given a specific research project, or sub-project related to issues of ocean data and model analysis. The student will be required to analyze and visualize the given data using matlab. Tasks may be specific as visualization data from a given section, to as general analyze the transport across several geographic domains using several different approaches and report back on the strengths and weaknesses of each approach, including comparisons of the calculations with other estimates in the scientific literature. Specifics will depend on the skills of the given student.
Skills required: A strong math, physics, engineering or computer science background is needed. Ability to work in linux based environment. Familiarity with writing analysis and visualization scripts in matlab. A desire to be a member of a larger research team and interact with researcher involved in different projects to learn about the oceans and our environment. Strong verbal communication skills. Self-Motivated. Familiarity with the oceans, atmosphere or climate would be nice, but is not necessary.
8. Historical presence of microplastics in the atmosphere of Montreal (Canada)
The increasing presence of plastics and degradation products in our environment poses concerning environmental and health issues. However, our understanding of their fate, especially in the atmosphere and surface water reservoirs is still under progress. In this project we are proposing to use an approach coupling characterization, chemistry, and isotope geochemistry to study i) the historic presence of atmospheric microplastics in Montreal since 1968 (concentrations and spatial distribution), ii) the degradation of these microplastics in the atmosphere and in surface waters, and iii) the potential contribution of degraded atmospheric microplastics to the levels of urban atmospheric CO2 and dissolved organic carbon in the St Lawrence River.
Research area, student roles & skills
Research area: I am a professor in geochemistry, with an expertise in isotope geochemistry. My fields of research mostly focus on environmental quality. My research group and I study the source and the fate of environmental contaminants.
Student roles: Drawing on our access to the city unique aerosol filter archive that covers all the Réseau de Surveillance de la Qualité de l’Air (RSQA) air monitoring stations disseminated on Montreal Island, the student will achieve a high-frequency, high-intensity observation study of the presence and spatial distribution of atmospheric microplastics (AMPs) in Montreal since 1968. For that period (1968-2025) monthly (collected on precleaned quartz filters; during the first week of each month) archive TSP (Total Suspended Particles) and PM10 (aerodynamical diameter ≤5 µm) samples from 5 representative RSQA monitoring stations will be analyzed for their microplastics characteristics and concentrations: 1) Sainte-Anne-de-Bellevue, representing regional background, 2) Downtown Montreal, representing the city average pollution levels, 3) Saint-Jean-Baptiste, downwind the island’s petrochemical activities, 4) Échangeur Décarie (for samples collected prior to 2024), located along one of the city’s most important highway hubs and Blvd Pitfield (since 2024), within 10 meters of highway 13, and 5) Hochelaga-Maisonneuve, neighbouring port activities. For each filter sample, using McGill’s hollow-laser desorption/ionization – mass spectrometry (HoLDI-MS) platform the student will determine both its aerosol chemical composition and relative quantity of AMPs.
Skills required: A background in either Environmental Sciences, Earth Sciences, Chemistry or Atmospheric Sciences is recommended.
9. Identifying the Intensity and Frequency of Hurricane Events in Atlantic Ocean
Supervisor: Xander Wang
University: University of Prince Edward Island (Charlottetown campus)
The Atlantic Ocean has long been recognized for its powerful and catastrophic storms. These powerful tropical cyclones, which are characterized by strong winds and torrential rain, pose substantial problems to coastal communities bordering the Atlantic basin. In recent years, there has been rising concern about the increasing intensity and frequency of hurricanes in this region, which is being driven by the complex interplay of natural climatic variability and human-caused climate change. Understanding these trends and their implications is critical for assessing the risks faced by vulnerable communities, developing effective disaster response strategies, and informing future planning and adaptation efforts. This research project will investigate the fundamental issues given by the severity and frequency of hurricanes in the Atlantic Ocean, as well as the methodologies used to determine spatial and temporal changes in these extreme weather events. Deepening understanding of these trends will help policy-makers in towards building resilience and mitigating the impacts of hurricanes in the face of a changing climate.
Research area, student roles & skills
Research area: The research team focuses primarily on water science and global climate change, including but not limited to the hydrological cycle, water resource planning and management, remote sensing, artificial intelligence, climate change adaptation, irrigation water management, socioeconomic change, and reservoir operating management.
Student roles: Students involve in this project will collaborate closely with postgraduate students (MSc, PhD, and Postdoc) from the Climate Smart Lab at the Canadian Centre for Climate Change and Adaptation in St. Peter Bay, Prince Edward Island. They will be able to explore more about climate data and working on scientific reports. During the appointment, the students could be assigned additional responsibilities such as data gathering and analysis, literature review, preparing coding, etc.
Skills required: Students should have environment and climate change backgrounds or related areas. Have a strong knowledge of data analysis and model application (for example, Microsoft Office, ArcGIS, ArcGISPro, QGIS, R, Python, and so on).
10. Integration of artificial intelligence in numerical investigation of Floating Offshore Wind Farms
Supervisor: Jahrul Alam
University: Memorial University of Newfoundland (St. John's campus)
One of the challenges to construction of floating offshore wind farms is that we do not have appropriate experimental facility. You are not able to go out to sea, build a test wind farm, and examine how it performs because it is highly expensive. Existing numerical and experimental tools do not capture the scale gap between the giant wind farms in harsh weather condition and the laboratory scale models. The project combines Large Eddy Simulation and deep neural networks to develop a new computational framework providing a digital twin of floating wind farms. By integrating both computational and artificial intelligence methods, which are currently being used independently in various fields, this project aims to develop the foundation for a digital twin of large floating wind farms in the harsh condition of Northwest Atlantic. The idea is to combine the knowledge of Navier-Stokes equation, scale-adaptive turbulence model, deep learning framework, and anomaly detection in order to understand physical processes that essential to develop digital twins.
Research area, student roles & skills
Research area: A primary focus of this research program lies in understanding turbulent fluid flows and their interactions with the environment. The research bridges fundamental investigations in turbulence with applied research, addressing challenges in atmospheric turbulence, wind energy, and nonlinear dynamics. This research group focuses on the development and testing of innovative computational fluid dynamics methods, including large eddy simulations, wavelet-based adaptive numerical methods, and deep learning of complex systems. The team collaborates with investigators from Mechanical Engineering and Physical Oceanography to help unlock the vast potential of wind energy by understanding wind-wave-turbine interaction in floating wind farms.
Student roles: For the first intern, the main role involves running the model, extracting the data, and visualization of the model output are primary role. Intern will prepare a short report of the research conducted during the internship. The role also involves learning research methodology and conducting literature review.
For the second intern, training, validation, and testing machine learning models are primary role. The intern is expected to conduct a literature review in order to gain basic knowledge of how to integrate machine learning technique with standard wind energy modelling techniques. Intern will prepare a github repository with test cases.
Skills required: The project aims to engage two interns. One intern is expected to have basic knowledge of running LES models remotely in a high performance computing cluster and visualizing the data through Paraview/Python environment. An educational background in mechanical engineering, physics, or applied mathematics would be ideal. The second intern is expected to have basic knowledge of machine learning, such as supervised learning, unsupervised learning, and generative modelling. An educational background in statistics, data science, or computer science is ideal
11. Non-Invasive Biomass Harvesting and Concentration Monitoring in Microalgae Photobioreactors
Supervisor: Mohamad Araji
University: University of Waterloo (Cambridge campus)
This research investigates the integration of microalgae photobioreactors in buildings to improve solar energy utilization through biomass production, optimized harvesting strategies, and reduced thermal loads. It applies machine learning direct search methods to define photobioreactor configurations, harvesting thresholds, and their thermo-physical, optical, and environmental properties. Key variables include solar altitude, shading conditions, culture growth kinetics, and harvesting rates. The goal is to maximize annual biomass yield and ensure predictable performance under light-limited conditions. Sensitivity analysis evaluates interactions among parameters such as panel thickness, tilt angle, harvesting frequency, and culture residence time using heuristic optimization methods. The results aim to support early-stage design and performance benchmarking of building-integrated photobioreactor systems. Engineering students can contribute through building energy modeling, programming, fluid analysis, and automation design.
Research area, student roles & skills
Research area: My research involves building science & technology, decarbonization & net-zero solutions, UAV infrastructure audits, non-destructive testing, image processing, and hyperspectral data interpretation. The scope includes areas such as enclosure systems, materials behavior, thermal and energy modeling, building audits, and machine learning applications.
Student roles: The duties can include: • Gather information from articles and published work to gain knowledge for performing research in the area of building-integrated photobioreactors with specific emphasis on shading dynamics, photosynthesis kinetics, and biomass harvesting methods. • Generate reports that summarize the outcomes and prepare presentations on data analysis. • Meet for discussion pertaining to research work and progress. • Work on collaborative tasks that require teamwork. • Setup physical or numerical models using MATLAB or similar tools, incorporating color analysis, image processing, and harvesting triggers.
Skills required: • Capable of research, learning, and mastering new techniques in biological kinetics, fluid extraction, and building performance optimization work. • Effective communication skills, both verbal and written. • Ability to work effectively both with supervision and independently. • Programming, scripting and coding in Matlab and similar software.
The objective of the project is to determine the long-term climate variability of the Canadian Prairies. Natural variability is a major component of our warming climate. We will use instrumental and proxy (tree-ring) data to examine climate trends and cycles over the past millennium. The statistical properties of the present and past climate regimes will be compared to the climate variability simulated by global and regional climate models. This combined analysis of modelled and observed climate data will inform decision-making on climate change adaptation in the Prairie Provinces. Key deliverables will include reports, scientific papers, and presentations at conferences and workshops. The student interns will be involved in disseminating the project results.
Research area, student roles & skills
Research area: Our specialized research area is the science of climate change and variability. We study past climates using information from tree rings. We study the future climate by analyzing data from climate models. Our reconstructions of past climate and projections of future climate are used to assess climate risks and to plan adaptation to minimize them.
Student roles: The student interns will assist with research in our tree-ring lab, extracting data from tree rings, and analyzing these data for climate signals. They will also be involved in processing data from climate models and developing climate change projections to support adaptation planning.
Skills required: The student interns should have an educational background in natural science or environmental engineering. They should have computing skills and the ability to process and analyze large sets of numerical data.
13. Resilience of energy infrastructure under sever climate-induced loading
Supervisor: Atef Mohany
University: Ontario Tech University (Oshawa campus)
As climate change intensifies extreme weather, improving the resilience of energy infrastructure is essential for reliable and sustainable power delivery. This project will investigate the impacts of extreme wind and ice storms on solar panels, wind turbines, and transmission cables. The student will study how extreme weather contributes to failure risks, including panel uplift, turbine blade imbalance, and cable galloping. Using simplified modeling, simulations, and case studies of past storms, the project will identify vulnerable components and evaluate mitigation strategies.
Research area, student roles & skills
Research area: My research is interdisciplinary, spanning aerodynamics, acoustics, vibration, and flow-induced vibrations.
Student roles: The successful candidate will work closely with my research group to study the resilience of energy infrastructure under severe climate-induced loading.
Skills required: A background in structural dynamics and vibration is an asset.
14. Understanding icebergs and polar oceans using advanced computing systems
Supervisor: Juliana M. Marson
University: University of Manitoba (Winnipeg campus)
Icebergs play a critical role in both human activities and the polar environment. They pose hazards to navigation and offshore operations, while also transporting vast amounts of freshwater and (potentially) nutrients that influence ocean ecosystems and circulation. Understanding where icebergs travel and how long they survive is therefore important for both operational forecasting and climate science.
This project will investigate how icebergs are represented in the NEMO (Nucleus for European Modelling of the Ocean) ocean model and how improvements to that representation affect simulated iceberg behaviour. Working with state-of-the-art numerical model output, students will analyze large datasets to evaluate whether these developments lead to more realistic patterns of iceberg distribution and longevity in the Pan-Arctic and, potentially, the Southern Ocean.
The internship will provide hands-on experience in polar research, scientific programming, and the analysis of large geophysical datasets. Students will gain exposure to Earth system modelling techniques that are widely used in climate and ocean research, while contributing to our understanding of the role of icebergs in the changing polar regions.
Research area, student roles & skills
Research area: My research focuses on polar physical oceanography and the interactions between the ocean and land ice, including icebergs, glaciers, and ice shelves. I investigate how increasing meltwater input from a warming climate affects ocean circulation, water properties, and marine ecosystems. My particular expertise is in icebergs: how they drift, melt, and influence the ocean around them. Using high-performance computing systems and advanced ocean models, I study processes that are difficult to observe directly, helping improve our understanding of how polar oceans are responding to environmental change.
Student roles: The student will work with large model-generated datasets, performing data analysis, visualization (plots and maps), and interpretation of results. They will gain hands-on experience with ocean modelling and high-performance computing (HPC) systems used in polar research. The project will also provide exposure to physical oceanography concepts and methods, particularly in the context of ice–ocean interactions in a changing climate. In addition, the student will be involved in research workflows typical of the field, including team work, data processing and preparation of results for publication in a peer-reviewed scientific manuscript. This role offers training in both computational and Earth system science.
Skills required: Students should have some experience with computer programming (e.g., Python, Matlab, Fortran, or similar) and a basic foundation in mathematics, physics, and/or physical geography. Prior coursework in oceanography is an asset but is not required. Applicants with stronger quantitative and programming skills who are interested in learning oceanography are encouraged to apply, as are students with a stronger background in oceanography and only limited coding experience. Curiosity, enthusiasm, and a willingness to learn are valued as much as previous experience.