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Faculty of Science Project Summaries

Supervisors

Supervisor name: Ali Neshati

Project title: Data Visualization and Interaction on Smartwatches

Summary of research project: This research enhances smartwatch functionality by improving data visualization and interaction on small screens. It focuses on user-friendly solutions, including dynamic visualizations for complex data and intuitive touch and voice-based interactions to address the "fat finger problem." These advancements aim to transform smartwatches into more effective tools for real-time data analysis in healthcare and decision-making in business. Overall, the study seeks to significantly boost smartwatch utility, impacting everyday use and advancing the field of wearable technology.

Student responsibilities/tasks:

Students in this smartwatch research project will:

Conduct literature reviews on wearable tech.
Design and conduct user studies.
Develop space-efficient data visualizations.
Prototype touch and voice interaction techniques.
Analyze data and report findings.
Collaborate in teams.
Prepare research documentation and dissemination materials.

Student qualifications required:

Completed courses in Human-Computer Interaction (HCI).
Minimum grade: B+ in relevant courses.
Strong programming skills (e.g., Python, JavaScript).
Experience with user interface design tools.
Basic knowledge of statistical analysis.
Effective communication and teamwork skills.

Expected training/skills to be received by the Student:

Gain advanced skills in Human-Computer Interaction (HCI) and data visualization, focusing on creating user-friendly interfaces for small screens.
Develop proficiency in programming languages like Python and JavaScript, specifically tailored for wearable technology and smartwatch applications.
Acquire hands-on experience in conducting comprehensive user research studies, including data collection, analysis, and applying findings to design.
Learn data analysis and statistical techniques, essential for interpreting user study results and making data-driven design decisions.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Andrea Kirkwood

Project title: Investigating biotic interactions between invasive and native aquatic plants

Summary of research project: Aquatic vegetation is essential for healthy aquatic ecosystems, however, when humans add too much nutrients to surface water bodies, this causes excessive vegetation growth. Too much vegetation growth can choke out navigation routes and cause water quality decline when the plants die and decompose. Additionally, non-native invasive species tend to be the dominant members of the plant community, and out-complete native species. The proposed project aims to gain a better understanding of the interactions between native and non-native aquatic plants to understand why some native plants are more vulnerable or resistant to non-native invaders.

Student responsibilities/tasks:

The student would conduct field and lab research from May-August with a graduate student working in the same study lakes.
The student would take field measurements and collect plant material to take back to the lab for processing.
Data input and analyses would also be part of this research project.

Student qualifications required:

Students currently in 3rd year with a minimum GPA of 3.0 will be prioritized.
Ideally, student applicants would have taken an ecology-related course such as BIOL 3660 or BIOL 3640.
Previous field or lab experience is an asset.
The ability to do light physical activities on a boat in inclement weather is required.

Expected training/skills to be received by the Student:

Take field measurements (e.g., temperature, pH, etc.) and collect biological samples.
Process water and biological samples in a laboratory.
Learn data management and analyses.
Work in a team environment.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Annie En-Shiun Lee

Project title: Empowering Agriculture through AI-Driven Data Analytics Solution

Summary of research project: Despite advancements in Big Data and AI, agriculture faces challenges due to limited access to relevant information in local languages. This data science project targets multi-language generation by integrating non-traditional sources, notably data from a local call center. Objectives encompass AI-driven multilingual queries, hot spot identification, and predictive analysis. The plan integrates a user interface for multiple languages and time series with the ultimate goal of empowering farmers with actionable visualization insights, addressing information gaps, and enhancing informed decision-making.

Student responsibilities/tasks:

GOAL: create comprehensive and reliable dataset for visualization insights.

Building a data corpus from variable sources.
Data quality assurance.
Data Preprocessing and Cleaning.
Data Aggregation and Analysis via Exploratory Data Analysis.
Development of AI-based modules encompassing language and spatiotemporal models.
Development of visualization platform for AI-Driven Insights.

Student qualifications required:

Minimum grade: B+ in relevant courses.
Strong programming skills in Python.
Experience with user interface design tools.
Basic knowledge of statistical analysis.
Effective communication and teamwork skills.
Courses: Scientific Data Analysis (CSC2000)
Preferred Courses: Information Visualization (CSCI 4210), Machine Learning, Theory and Application (CSCI4050)

Expected training/skills to be received by the Student:

Research planning and design: drafting proposal, research ethics review, data and code management plan, and project management.
Empirical experimentation: experimental design, data bias, and fairness, baseline reproducibility, statistical analysis.
Software development: advance code packages, debugging and versioning, agile scrum framework.
Quantitative modelling: data science lifecycle/workflow, text pre-processing, machine learning models, data mining and information retrieval, multilingual language models.
Scientific communication: reading and reviewing the literature, preparing the manuscript, presenting and disseminating research.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Annie En-Shiun Lee and Christopher Collins

Project title: A Human Error Correction Interface for Translations

Summary of research project: Language is how we connect with one another to share ideas, conduct business, and build communities. The natural language processing field has been recently dominated by widely-adopted machine learning approaches that are data and compute-hungry that is only applicable to a handful of languages, thus causing a growing disparity for the remaining more than 7000 languages in the world (Josh et al 2020). Therefore we propose building an Error Correction Human Interface for Translations, allowing people to interact with translations by offering translated word/phrase options with correction selections in order to get the most appropriate translation thus creating a high-quality dataset.

Student responsibilities/tasks:

Create a comprehensive Data Management Plan and Project plan.
Curate and create a text translation dataset by web scrapping, pre-processing, and cleaning.
Research, design, and implement basic minimal viable product of user web interface for making translations.
Design and implement interactive translated word/phrase options and error correction.
Web programming, text pre-processing and cleaning, python coding skills.

Student qualifications required:

Web programming expertise for designing and implementing interactive user interfaces.
Proficient in web scraping techniques, text pre-processing and cleaning.
Familiarity with data management and project planning.
Interest in natural language processing or related field.
Excellent communication skills and problem-solving abilities

Expected training/skills to be received by the Student:

Research planning and design: drafting proposal, research ethics review, data and code management plan, and project management.
Empirical experimentation: experimental design, data bias, and fairness, baseline reproducibility, statistical analysis.
Software development: advance code packages, debugging and versioning, agile scrum framework.
Quantitative modelling: data science lifecycle/workflow, text pre-processing, machine learning models, data mining and information retrieval, multilingual language models.
Scientific communication: reading and reviewing the literature, preparing the manuscript, presenting and disseminating research.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Brad Easton

Project title: Advanced materials for electrochemical energy storage and conversion

Summary of research project: The development of conductive metal-oxide-based materials supports represent a unique platform for electrochemical energy conversion and storage applications. This project will involve the synthesis and characterization of various nanostructured metal-oxide that are doped with different metals in order to modify their electronic properties and morphology. Detailed physical characterization will be performed to relate electrochemical performance with structure/composition.

Student responsibilities/tasks:

The student will work in collaboration with other team members on the synthesis and characterization of the materials.
Materials characterization methods will include SEM, XPS, XRD, TGA, BET, and UV-Vis spectroscopy.
The student will be responsible for electrochemical testing and evaluation of the materials.

Student qualifications required:

Chemistry majors.
Successful completion of CHEM 3540U and CHEM 3040 is considered an asset.

Expected training/skills to be received by the Student:

Nanomaterials synthesis.
Electrochemical measurements.
Analytical chemistry.
Surface chemistry.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Denina Simmons

Project title: Summer field research

Summary of research project: The objective of the research is to determine the effects of perflourinated alkyl substances (PFAS) on fish downstream from GTA airports, including Oshawa Creek, Beaver Creek, and Etobicoke Creek. This project requires a URA student to help the PhD Graduate student lead. We will be setting minnow traps in creeks, collecting fish apidermal mucus from those fish, and then we will release the small fish back into the creeks without harm. The proteome and metabolome will be identified in the collected mucus samples to understand the effects of PFAS on small bodied fish in Lake Ontario Tributaries.

Student responsibilities/tasks:

The student will learn to set minnow traps, collect fish epidermal mucus, and analyze mucus samples for proteins and metabolites.

Student qualifications required:

All training provided, we want a student who is available all summer and likes to be outdoors, can swim, and will have fun in the water handling small fish.

Expected training/skills to be received by the Student:

Setting minnow traps.
Sampling fish mucus.
Proteomics.
Metabolomics.
Chemoinformatics.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Fedor Naumkin

Project title: Simulation of internal processes in molecular complexes

Summary of research project: Molecular transformations lead to formation of new substances at nano-scale.

The project's objectives can include:

Analysis of structure and properties of polyatomic systems composed of a few molecular fragments;
Prediction of their internal-field mediated shape-alterations and of related potential-energy barriers;
Investigation of species' geometry variation with their charge state;
Modeling of systems' IR spectra to facilitate their experimental identification and to follow related processes.

Such systems are relevant for various practical uses involving optical properties and solar-energy utilization, electric-field assisted reactions, molecular photonics and machinery.

Student responsibilities/tasks:

Learn and use a computational chemistry software (under guidance of supervisor), prepare input files, run multiple calculations on available high-performance computing facilities (accessed remotely via student's laptop).
Analyze and visualize results, in particular by using a molecular-graphics software (to be learned), regularly discuss them with supervisor.
Do a literature search, prepare and deliver presentations (at the Research Day, etc.)

Student qualifications required:

Preferred: completed CHEM 2010 (Structure & Bonding) course with at least B- grade.
Proficiency in main MS Office applications (Word, Powerpoint, Excel).
Good calculus and geometry skills, good 3D imagination being a plus.
Interest in a computational research is a bonus.
Familiarity with work in a Linux environment is an extra bonus.

Expected training/skills to be received by the Student:

Direct experience in modern molecular modelling using state-of-the-art professional quantum-chemistry software at high-performance computing facilities.
Development of practical skills in a highly prospective area of computational nanochemistry, which adds a competitive edge for future employment.
Exercising the abilities to think independently, to efficiently check ideas via reliable calculations, to predict new species and/or processes to guide experiments.

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Hendrick de Haan

Project title: Computational Physics Research: Particles Jumping Around Can Model Stuff

Summary of research project: Using basic programming skills (please be familiar with for loops, reading/writing data files, etc.), we will create simulations of particles moving around randomly. This might model nanoparticles suspended in solution...bacteria on a surface...red blood cell aggregation. The details will be determined based on the student’s interest, my interest, and other activities in my research lab. Regardless, we will explore techniques for analyzing stochastic processes and try to learn something fundamental about a particular application. A primary learning outcome will be for the student to gain experience using computers to do scientific research.

Student responsibilities/tasks:

The student will be required to write programs to read data and perform analysis on that data.
The student will then visualize the results of the analysis.
Depending on the project, the student might also be involved in generating the raw data - e.g., by running simulation code.
The student will be part of the process of interpreting the results and formulating insights into the system under study, but the student is not expected to do this alone.

Student qualifications required:

Basic programming skills (for/while loops, conditionals, read&write data files, etc.).
1st year physics knowledge (basically, F=ma).
Bio-knowledge is a plus, but not required.
Desire to develop data analysis skills.
Enthusiasm for learning new things.
Self-motivation to continue advancing the project without minute by minute supervision.

Expected training/skills to be received by the Student:

Data analysis skills: The student will develop a workflow of i) writing programs that read data files and perform (statistical) analysis on the data to produce quantities of interest ii) plotting scripts to plot these quantities vs. independent variables iii) drawing conclusions from this data and iv) ultimately generating insight into the system under study.
Gain experience in working as a part of a research team: While the student will have their own defined project, it will almost certainly be part of a larger research area of the lab. Thus, the student will both learn to work independently to advance their project and to work as a team to integrate these results with other projects and synthesize these to generate deeper insights. Working with other undergraduate and graduate students in the lab will also provide a great resource to get help when stuck on a particular issue in their research.
Modelling: The student will be involved in developing simplified models of complex (usually biological) systems. This is a core tenant of the typical physics reductionistic approach and the student will learn the strengths of this approach (surprisingly general insights from a simple model) and its weakness (neglecting details that can impact the conclusions drawn from the simple model).

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Jane Breen

Project title: Markov chains, graph theory, and dynamic processes in a network

Summary of research project: A Markov chain is a mathematical model which has applications to many domains, including robotic surveillance, earthquake sequencing, and molecular conformation dynamics. For many such applications, there is an underlying network constraint. For example, we can model the spread of disease as a random process on a contact network. Similarly, one can model the spread of fake news in an online social network, or vehicle movement in an urban road network.

This project will explore the applications of graph theory and Markov chains to a wide range of application domains. The specific area of application will be chosen by the student based on their interests, background, and career aspirations.

Student responsibilities/tasks:

The Student will contribute to a research program that explores the applications of graph theory to a wide range of application domains. The specific area of application will be chosen by the Student based on their interests, background, and career aspirations.
The Student will undertake some background reading on network science, Markov chains, and graph theory, develop simulation models, and analyze and model real data using these techniques.

Student qualifications required:

Minimum 3.0 GPA.
Some programming experience is preferred, but not necessary.
Courses: Discrete Math (MATH2080U/CSCI2110U) and some linear algebra preferred, but not necessary. MATH3090U (Network Science) is a bonus.

Expected training/skills to be received by the Student:

Problem-solving skills and critical thinking.
Mathematical modelling.
Working with data to form and test conjectures.
Scientific writing skills, both formal (writing reports) and informal (writing blog posts, etc.).

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Janice Strap

Project title: Unraveling the molecular role of a multifaceted regulatory protein in the bacterial cellulose synthesis pathway

Summary of research project: This research project focuses on understanding the biosynthesis of bacterial cellulose, a crucial extracellular biopolymer found in the biofilm of many Gram-negative bacteria. Despite its significance in host-microbe interactions, the regulatory network controlling this process remains poorly understood. The primary objective is to elucidate the function of a putative regulatory protein believed to play a pivotal role in cellulose biosynthesis. Employing a combination of molecular biology and biochemical analyses, the study aims to decode the molecular mechanisms of this protein and its impact on the cellulose biosynthetic pathway.

Student responsibilities/tasks:

Responsibilities include conducting experiments & documenting research activities in a laboratory notebook.
Techniques that will be used in this project include aseptic technique, bacterial culture, sterilization, sample preparation, mutagenesis, complementation, cloning, analytical biochemical assays, gel electrophoresis, polymerase chain reaction, DNA/RNA extraction.
Students will be required to complete biosafety and WHMIS training.

Student qualifications required:

Applicants are expected to have successfully completed BIOL 2060U and BIOL 3080U with a minimum grade of B+.

Expected training/skills to be received by the Student:

Experimental planning and organization.
Documentation of research activities.
Data analysis and interpretation.
Standard laboratory practices, maintenance and procedures.
Teamwork.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Kevin Coulter and Liliana Trevani

Project title: Synthesis of Mo(dithiolene)2 complexes with Hydrophobic Tails for Electrocatalytic Reduction of Carbon Dioxide

Summary of research project: Co-supervised by K. Coulter and L. Trevani. The rapidly progressing field of “Solar Fuels” research seeks to develop commercially viable catalysts for the electroreduction of atmospheric carbon dioxide to renewable fuels. The ultimate goal of this project is to synthesize dithiolene ligands with hydrophobic tails that can be covalently linked to the surface of carbon electrodes, thereby mimicking the key features of natural enzymes that reduce CO2. The student will perform preliminary testwork on (i) the synthesis of phenylenediamine ligands with hydrophobic tails and conversion to dithiolene ligands and (ii) covalent linking of simple aromatic amines to carbon electrode surfaces.

Student responsibilities/tasks:

Understanding the basic theory and goals of the project.
Safety Training and reviewing Safety Data Sheets of chemicals to be used.
Conducting synthetic reactions and product purification steps (under close supervision in the lab to avoid hazards and mistakes).
Using standard chemical instrumentation to analyze product samples.
Reporting results in a thesis format.

Student qualifications required:

Completion of CHEM 2120 Organic Chemistry and CHEM 2220 Molecular Structure Determination is required.
Completion of CHEM 2030 Analytical Chemistry and CHEM 2040 Thermodynamics and Kinetics is preferred.
This project can and will be adjusted to the level of the student hired.

Expected training/skills to be received by the Student:

Develop awareness and understanding of the engaging field of Solar Fuels research and associated commercialization to mitigate Global Warming, and future employment opportunities.
Experience with conducting an independent laboratory research project, ie. analyzing new results and deciding what to do next, instead of just following a rote procedure within episodic 3 hr lab periods.
Development of organizational skills, having to plan experiments including the time needed to complete the work.
Learning of new lab techniques and instrumentation, for example Nuclear Magnetic Resonance spectroscopy, materials synthesis and characterization, electrochemistry.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Lennaert van Veen

Project title: Modelling swarms of bacteria

Summary of research project: Bacteria can organise in swarms in various ways. In this project, we look at "twitchers", bacteria that move in a jerky, random way. They interact with others only by bumping into them and aligning due to their oblong shape. If the density is great enough, they can form "rafts", patches of high density that may be the precursor to biofilm formation. The two main ways to model this process are Agent Based Simulation, based on a large number of "agents" that represent individual bacteria, and continuum modelling. We will try to connect ABM and continuum modelling and distill the parameters and initial conditions for the latter from the former.

Student responsibilities/tasks:

Initialize and run both ABM and continuum simulations.
Develop visualization and data analysis tools.
Implement and test various interaction mechanisms.
Help design targeted series of simulations to estimate parameters.

Student qualifications required:

Essential: a firm grasp of Ordinary Differential Equations, Computational Science I, first-year physics, Phython/NumPy/MatPlotLib.
Bonus: experience with Numba, stochastic dynamics, GPU computing.

Expected training/skills to be received by the Student:

Mathematical Modelling: the cycle of trying out a model, interpreting the outcome, understanding what went wrong and formulating a better idea.
Parameter Inference: in many real-world problem the essential difficulty is that, while we have models to help us predict outcomes, we do not know the correct model parameters. You will find ways to estimate parameters based on carefully planned experiments.
Code Optimization: in order to estimate parameters and connect the two simulation modalities we will need to run a large number of large simulation. At that stage, we will carefully profile and streamline the Python code.
Visualization: the simulation results take the form of a time series of gridded data for a function of three variables. My current visualization is crude and can certainly be improved - but how?

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Liliana Trevani

Project title: Development of catalyst materials for the synthesis of hydrogen peroxide

Summary of research project: The student will explore new materials for the synthesis of hydrogen peroxide using electrochemical methods. Although this approach has shown numerous advantages over traditional chemistry methods, some problems, such as the cost and stability of the electrode materials, need to be solved. In this project, the synthesis and characterization of low-cost catalysts will be carried out using methods implemented in our group for the synthesis of non-precious catalyst materials for fuel cells that have shown a high catalytic activity towards the formation of hydrogen peroxide. Several characterization techniques will be used to evaluate the materials' performance.

Student responsibilities/tasks:

The student will characterize the synthesized materials using Raman and Fourier Transform Infrared Spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), nitrogen gas adsorption for surface area and porous size determination, and several electrochemical methods.
The student will investigate the impact of the reaction conditions, such as pH and temperature, on the electrochemical process.

Student qualifications required:

CHEM 3040 and CHEM 3530 (minimum grade B-).

Expected training/skills to be received by the Student:

Hands-on experience in several wet-laboratory procedures.
Synthesis of polymer gels.
Training on basic electrochemical techniques.
Training in Raman spectroscopy.
Training in data analysis, writing reports and presentation skills.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Mehran Ebrahimi

Project title: Machine Learning Techniques for Medical Image Fusion

Summary of research project: Several medical image processing techniques have been found to be useful for computer aided diagnosis (CAD). The goal of this Project is to extend and validate machine /deep learning algorithms aimed at multimodality image fusion. The research will be conducted at the Imaging Lab in the Faculty of Science, Ontario Tech.

Student responsibilities/tasks:

The Student will be responsible for utilizing and extending our current image processing, machine learning, and data visualization tools and algorithms in either Matlab or Python.
The Student will be engaged in the literature review, mathematical modelling, programming, and validation of the results.
In addition, the Student is expected to produce scientific reports of the results in the form of a poster and/or a conference paper.

Student qualifications required:

The Student is required to have a good understanding of calculus and linear algebra.
Programming skills in Python or Matlab is required.
Experience working with imaging data, machine learning, and optimization is desirable but not required.
Students in Computer Science, Mathematics, Physics, or a related field are encouraged to apply.

Expected training/skills to be received by the Student:

Image Pre-processing.
Mathematical Modelling.
Numerical Simulations.
Data Analysis.
Document/Report/Manuscript preparation.

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Nisha Agarwal

Project title: Tailored fabrication of quantum dots for photovoltaic devices for use in bio sensing

Summary of research project: There is a need for point-of-care devices that can help detect quantities of a specific biological material. A current or voltage response would be ideal for such a biosensor.

During this project, the intern will synthesize quantum dots made of gold and silver and deposit them on surfaces to build a functional photovoltaic device. They will tailor the morphological surface properties in unique arrangements to look for maximum absorption. Optical properties will be characterized using UV visible absorption spectroscopy. Finally, they will deposit biological material, in our case hemoglobin on these photovoltaic devices to determine if hemoglobin concentration can be quantified.

Student responsibilities/tasks:

The student will synthesize quantum dots, deposit them on surfaces with different morphology, build them into photovoltaic devices and test them as biosensors.

Student qualifications required:

The student must be pursuing their third year undergraduate degree in either Physics or Chemistry.

Expected training/skills to be received by the Student:

The student will learn how to synthesize quantum dots and build a photovoltaic device.
They will be able to build and test a biosensor.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Sean Forrester

Project title: Cys-loop Receptors in Canine Heartworm

Summary of research project: A parasite important in Canada with the potential for higher prevalence due to climate change is the canine heartworm Dirofilaria immitis. Our laboratory conducts research on a large group of cys-loop receptors called ligand-gated chloride channels (LGCCs). However, despite a relatively large family of receptors, we know very little about how these channels contribute to the biology and parasitic lifestyle of D. immitis. This information is essential for determining whether these channels have potential as future nematocidal drug targets. This Project will investigate the biological role of the cys-loop LGCC family in D. immitis.

Student responsibilities/tasks:

PCR.
Gel electrophoresis.
Plasmid gene cloning.
Bacterial transformation.

Student qualifications required:

BSc student in Biological Science or Chemistry.
GPA or 3.7 or higher

Expected training/skills to be received by the Student:

Student will learn basic molecular biology skills and proper techniques
Student will learn how to troubleshoot experiments
Student will gain a breath of knowledge related to molecular parasitology

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Theresa Stotesbury & Jean-Paul Desaulniers

Project title: Developing new optically active biomaterials

Summary of research project: At the interface of chemical biology and materials science sits the exciting field of smart biomaterials, where scientists get to play with designing soft materials that can interact with many aspects of our everyday lives. Some contain biomolecules like nucleic acids, proteins, and/or enzymes that can be useful for applications in biosensing, drug delivery systems and even tissue engineering. There is little research on how chemical modifications to these biomolecules influence the physicochemical properties (e.g. optical activity, stability). In this project, the candidate will work on developing biomaterials that contain either melanin and/or chemically modified oligonucleotides (TBD).

Student responsibilities/tasks:

Understand the research landscape through literature review
Independent wet-bench experimentation in chemical biology & materials chemistry
Independent and safe operation of analytical instrumentation
Prepare written reports and oral research presentations
Contribute to positive laboratory culture

Student qualifications required:

We are actively seeking applications from upper year (e.g. 3rd year) chemistry or forensic chemistry undergraduate students. If you do not meet this please explain your skills in organic synthesis, analytical instrumental analysis, and overall chemical literacy.
Apply to both Dr. Stotesbury and Dr. Desaulniers.

Expected training/skills to be received by the Student:

Chemical synthesis.
Biomaterials synthesis.
Instrumental analysis.
Data interpretation.
Knowledge mobilization.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR