NSERC Undergraduate Student Research Awards

If you are an undergraduate student and you would like to get research experience in an academic setting, consider applying for an Undergraduate Student Research Award (USRA) administered by the Natural Sciences and Engineering Research Council of Canada (NSERC)

Through this award, eligible professors receive a subsidy to hire students to work on interesting research-related jobs and projects 16 weeks of research over the Summer 2024 term.

Please note awards made to self-identified Indigenous and Black students are in addition to the department's allocation.

Please review the list of available research projects and potential faculty supervisors at the bottom of this page. 

Duration: 16 consecutive weeks 

Value of the award: Each award is $6000 NSERC (USRA) + a minimum of $1500 provided by the NSERC grant holder (supervisor).

Application Deadline: April 1, 2024

Application instructions:

Review the list of potential supervisors and their research projects at the bottom of this page.

Review the USRA Application Checklist. n.b. There are separate sections for students and faculty supervisors to complete. 

Register for an account at NSERC's online system

Follow the instructions online on how to complete the forms.  View additional resources:

      Online Application Portal for Faculty Supervisors and Student Applicants
      NSERC USRA Instructions for Completing Form 202
      NSERC USRA Program Application Tutorial

You must also upload, with your application form, a copy of your official transcripts, including a copy of the grade legend on the last or back page.

Once completed online, scan a copy of both Part I and Part II forms, as well as your transcript to submit electronically to the Department of Physical and Environmental Science for review.

Submit the completed application package to: 
       DPES Undergraduate Team


2024 Potential NSERC USRA Faculty Supervisors & Research Projects

Assessing whether the experimental evolution of bacteria compromises beneficial functions of interest         

Terrence Bell   terrence.bell@utoronto.ca    

In our lab, we are interested in whether we can condition bacteria to thrive in unfamiliar environments. Our specific focus is on enhancing the efficacy of commercial probiotics for agriculture, which are targeted to reduce the need for chemical nutrient and pesticide inputs, in order to reduce the negative footprint of large-scale agriculture. However, we do not know whether such conditioning has negative, neutral, or even positive impacts on the microbial functions we are trying to promote, such as phosphorus solubilization and nitrogen fixation. In this project, we will assess this across different functions, bacteria, and timeframes.

 

Oxidative stress as a potential target of environmental contaminants: development of sex-specific cellular models

Elyse Caron-Beaudoin               elyse.caronbeaudoin@utoronto.ca                

Emerging evidence suggest that genes located on the sex chromosomes contribute to inherent sex differences in endogenous biological processes such as oxidative stress. This study aims to contribute to the field of co-culture development by devising sex-specific co-culture models to discover the sex-based effects of environmental exposures on oxidative stress pathways. Indeed, we do not understand the sex-specific impacts of many contaminants. In this NSERC USRA project, we will develop, validate, and use an airway experimental system to further our understanding of sex-specific oxidative stress response triggered by exposure to known inducers of oxidative stress.  The NSERC USRA student will use a variety of lab techniques, such as cell culture, ELISA and quantitative and digital PCR. 

 

Changes to soil organic matter chemistry with long term soil warming at the Harvard Forest      

Professor Myrna Simpson       myrna.simpson@utoronto.ca               

Globally, soils store twice as much carbon as the atmosphere. The world’s forests store ~45% of terrestrial carbon stocks and contribute to ~50% of the net primary productivity. Recent studies have demonstrated that these forests and their vast carbon reserves are under threat due to global environmental change. As part of this, warmer temperatures are shifting the balance of carbon stored versus carbon respired into the atmosphere.  Several soil warming studies have shown that these conditions enhance the microbially-driven degradation of soil carbon thereby increasing CO2 respiration into the atmosphere. It was previously believed that soil warming impacts on soil carbon dynamics would be short-lived because once microbes depleted preferred substrates, more difficult to degrade soil carbon components would not be able to support this heightened activity in the long-term.  However, long-term soil warming studies at the Harvard Forest (Petersham, Massachusetts) found continued and sustained net losses in soil carbon and flux of CO2 into the atmosphere. This observation was attributed to microbial reorganization which then facilitated the degradation of more complex carbon forms.  The mechanisms behind this process are unknown and there is a critical need for elucidating the fundamental mechanisms that control soil carbon dynamics in forests such that informed and accurate climate change mitigation strategies can be developed.  To fill this knowledge gap, this project will examine the fate of different soil organic matter components which are an important part of stored soil carbon whose stability can be impacted by warming.  This will include the study of different compounds that are hypothesized to be resistant to soil warming (cutin, suberin and lignin). Because these are biogeochemically complex, their fate with long-term warming is unknown.  To fill this knowledge gap, this project will examine lignin geochemistry in soils from the Prospect Hill experiment in the Harvard Forest which has been warmed at +5C above ambient for > 30 years.  Nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry will be used to measure the quantity, composition and oxidation state of different organic matter compounds.  This data will be used with other geochemical data to formulate a holistic understanding of long-term warming impacts on forest soils. 

 

Quantum computing for quantum chemistry: Algorithm development

Artur Izmaylov                 artur.izmaylov@utoronto.ca                   

The electronic structure problem is the key for material and drug designs. Solving it accurately using quantum mechanical methods on regular classical computers leads to algorithms whose execution time grows exponentially with the system size. Emerging technology of quantum computing recently provided a new hope to solve this problem efficiently. Yet, the new quantum hardware requires new algorithms. Currently, there are two main algorithmic frameworks for solving the electronic structure problem on a quantum computer: 1) quantum phase estimation (QPE) and 2) variational quantum eigensolver (VQE). None of these approaches provide a solution to the problem that is competitive with well-developed numerical techniques on a classical computer. Thus, none of them has yet demonstrated quantum advantage (superiority of quantum computing over its classical counterpart) for the electronic structure problem. In this project we will be developing new alternative frameworks for solving the problem on a quantum computer that will address main deficiencies of the previous techniques. The main goal is to develop a framework demonstrating quantum advantage in quantum chemistry problems.

 

Patterns of earthquake swarms in the Atlantic Ocean: what can data science teach us?                                    

Phil Heron         philip.heron@utoronto.ca      

The Mid-Atlantic ridge (MAR) is the largest geological feature on the planet, separating North and South America from Europe and Africa through plate tectonic processes. The movement of tectonic plates often generate large earthquakes and associated smaller aftershocks, as frequently recorded in Western Canada (due to oceanic material being pushed under North America) and recently highlighted by the February 2023 events in Turkey and through the 2010 devastation in Haiti.  Given the separation of tectonic plates at the MAR, the region experiences a lot of earthquakes.  However, over the past 30 years MAR earthquakes have often occurred in 'swarms' rather than as the standard large quake with smaller aftershocks. A swarm can be defined as a sequence of similar sized earthquakes occurring regularly over a relatively short time period (e.g., days or weeks or years depending on the frequency and regularity). Despite there being a large number of historical MAR swarms, there has never been a pattern analysis using data science techniques of this big data collection. In this project, a student will learn data science techniques and collate the different MAR swarm events to identify any patterns within the large amount of data. Based on such characterizations, the beginnings of a probabilistic framework for future tectonic events could also be created – can we predict where a swarm will occur next? 

 

Tracking the Origin of Rapidly Rising Releases of Hexachlorobutadiene to the Global Environment        

Frank Wania    frank.wania@utoronto.ca       

Hexachlorobuta-1,3-diene (HCBD) has been listed in the Stockholm Convention on Persistent Organic Pollutants (POPs), respectively, meaning that both deliberate and unintentional production should be curtailed globally. Yet, air-monitoring has revealed that HCBD concentrations have been rising rapidly and continuously from 2008 to 2021, making it the most prevalent POP in the global atmosphere. We will be using time-variant simulations with a global dispersion model to explore whether unintentional Chinese emissions associated with chlorinated solvent production can explain the temporal and spatial trends observed for HCBD in the atmosphere or whether additional emissions need to be invoked.

 

Wine grape responses to climate change across biological scales    

Adam Martin   adam.martin@utoronto.ca   

This project focuses on quantifying multiple aspects of wine grape responses to environmental change, including 1) photosynthetic temperature responses; 2) changes in leaf- and root physiology in response to changes in below-ground growing conditions; and 3) wine grape drought tolerance. The candidate will work alongside graduate-level researchers in a combination of lab- (UTSC) and field (Niagara Region)-based research.

 

The impact of heterogeneous mantle chemistry on planetary surface mobility

Julian Lowman                julian.lowman@utoronto.ca 

Planetary surface motion, like movement of the Earth's tectonics plates, is a surface expression of convection in the mantle. For several decades, seismic tomography has been accumulating evidence that Earth's deep mantle is not compositionally homogeneous. The study described here will investigate the feedback between a compositionally anomalous and intrinsically dense component in the deep mantle and surface mobility, using a state-of-the-art parallelized numerical model of mantle convection. The project will be entirely computational in nature and will require running simulations on the Digital Alliance Canada's high performance computers in consultation with the Principal Investigator, Prof. J. Lowman. Student responsibility will entail the submission of job scripts and the post-processing of data. Interested applicants should be enrolled in a Physics or Geoscience Program and have a strong interest in Earth and Planetary Science. Some knowledge of a programming language like Python, or an equivalent tool would be highly beneficial.

 

Permafrost thaw as a potential driver of unforeseen cyanobacteria blooms in Canada's North 

Irena Creed      k.erratt@utoronto.ca                   

Canada's northern regions are experiencing some of the world's most rapid rates of climate change, leading to transformational shifts in water security with crucial ramifications for human health and well-being. Unprecedented warming has altered permafrost characteristics, leading to widespread thawing and the release of nutrients and organic matter into downstream waters. These alterations are impacting the productivity and microbial diversity of northern aquatic environments, and have been proposed as a potential driver behind unforeseen cyanobacteria blooms. A transition to a cyanobacteria-dominated state is often undesirable, impacting aquatic food webs and the health and livelihoods of human communities reliant on these waterbodies (e.g., toxin production). What remains unclear is the underlying drivers promoting cyanobacterial blooms in northern landscapes and why some lakes bloom, and others remain unaffected. This study aims to characterize present and future cyanobacteria risks (e.g., field surveys and bioassays), as well as historical perspectives to reconstruct long-term records of environmental change (e.g., paleolimnology) in lakes in the Northwest Territories. Examining lakes with varying morphologies and underlying permafrost conditions can aid in understanding different lake sensitivities to cyanobacteria blooms in northern environments.

 

Designing an environment for new physics exploration and discovery.

Kristen Menou     kristen.menou@utoronto.ca    

Vision-Language Models (VLMs) have multimodal capabilities and emergent physics capabilities that should only increase as reasoning and planning skills improve in the future. This project will lay out the ground work for building a multimodal (vision + text) environment designed for the exploration and discovery of new (hidden) physics by a self-guided, strongly-capable AI system (VLMs and beyond). This environment may become a prototype benchmark for emerging AI Science capabilities.
Skill set: Python coding, Newtonian Physics, Data Science/ML 
References: 
https://gymnasium.farama.org/

 

Building datasets and benchmarks for a Physics simulation AI engine

Kristen Menou     kristen.menou@utoronto.ca    

Today's best Large Language Models have flawed but emergent capabilities in computational physics. This project will lay the ground workfor the creation of a new dataset that may eventually be used to train an AI assistant for computational physics.
Benchmark design will also be explored.
Skill set: Python coding, Computational Physics and Simulations, Data Science/ML 
References: 
https://arxiv.org/abs/2312.02091

Design and Synthesis of Metalloporphyrin as Electrochemical Catalysts for Green Energy

Xiao-an Zhang     xiaoan.zhang@utoronto.ca

Driven by the urgent challenge of global warming, a multitude of strategies have been devised to generate renewable chemical energy sources, aiming to replace conventional fossil fuels. In recent years, notable strides have been taken in catalytically converting carbon dioxide (CO2) into hydrocarbons or splitting water into H2 and O2 using solar energy or sustainable electricity. A diverse array of transition metal complexes, including Fe, Co, Cu, and Ni-based catalysts, has been developed and scrutinized for this purpose. Notably, metalloporphyrin stands out with unique advantages, encompassing high stability, efficient electron transfer, and structural adaptability. Our research,1,2 along with others, has demonstrated that modifying porphyrin structures or altering the metal species can lead to enhanced activities, reduced overpotential, and improved catalytic efficiency. 

In this USRA project, students will be engaged in our further exploration of the design, synthesis, and characterization of novel porphyrin ligands and their metal complexes, as well as testing their catalytic activities (in collaboration with Prof. Bernie Kraatz). We particularly encourage students with an interest in and training in organic synthesis to apply.

References:

  1. ACS Appl. Energy Mater. 2019, 2 (2), 1330;
  2. ACS Sustainable Chemistry & Engineering2020, 8 (25), 9549