Funded Scholarship in Canada for PhD and Masters Positions 2023-24

Funded Scholarship in Canada for PhD and Masters Positions 2023-24

Funded Scholarship in Canada for PhD and Masters Positions 2023-24

Funded Scholarship in Canada PhD Positions

TOPIC 1: DEVELOPMENT OF AN AI ALGORITHM FOR EXPRESSOMICS AND METABOLOMICS DATA ANALYSIS FOR THE EARLY DETECTION OF OVARIAN CANCER

TOPIC 2: DEVELOPMENT OF A BIOREACTOR PRODUCTION PLATFORM OF HIGH-VALUE THERAPEUTIC AGENTS FROM PLANT CELLS

DETAILED DESCRIPTION:

Context: In collaboration with Spheroid IA inc., this research project aims to address the challenge of early detection and personalized treatment of ovarian cancer, through the integration of machine learning, deep learning and kinetic metabolic models. Ovarian cancer is often diagnosed in advanced stages, which limits treatment options and reduces survival rates. Furthermore, the disparity between transcriptomic and metabolomic data poses a significant obstacle to the precise characterization of the metabolic profile of cancer tissues, thereby hindering effective treatment strategies.

Aim of the project: The long-term goal to which this master’s project will contribute is to develop a predictive and personalized medicine approach for ovarian cancer, by harnessing the power of omics data analysis and computational modeling. More specifically, this project aims to develop supervised machine learning algorithms for the identification of biomarkers making it possible to differentiate healthy individuals from those suffering from ovarian cancer. These algorithms, paired with an already developed metabolic model, will be designed, selected and trained using transcriptomic and metabolomic data obtained from patient blood samples in collaboration with hospitals and medical clinics. The algorithms can thus be validated according to their predictive capacity for individual metabolic phenotypes and the level of aggressiveness of patients’ cancerous tissues.

The importance of this research lies in its potential to transform our ability to diagnose and treat ovarian cancer.

Topic 3: Propagation of fault aseismic slip due to fluid injection in the subsurface

CONDITIONS:

• A good academic record.
• Experience in numerical modeling using finite element, finite difference, finite volume or boundary element is required.
• Knowledge of at least one programming language (Julia, Python, or C++) is required.
• Experience in modeling thermo-hydro-mechanical processes in porous media is recommended.
• Strong communication skills in English (a minimum IELTS score of 7.0 is required for international applicants).

DETAILED DESCRIPTION:

Fluid injection in the subsurface is recognized as a potential source of induced seismicity, notably aseismic slip along geological faults, and potentially – if not controlled – major induced seismic events that can endanger the population and cause considerable damage to infrastructure. To foster the development of energy technologies known to help mitigate the impacts of climate change, such as geothermal energy production or geological storage of carbon and hydrogen, it is essential to understand and prevent the risk of induced seismicity during field operations.

This doctoral project will study the propagation of aseismic slip along faults in response to transient fluid injection, taking into account thermal effects. The results of this project will contribute to the design of injection scenarios to mitigate the risk of induced seismicity and ensure sustainable and responsible use of subsurface resources. This work is primarily theoretical and numerical.

TOPIC4: PROSPECTIVE HUMAN ERROR ANALYSIS IN AVIATION FOR COMPLEX FAILURE SCENARIOS

Description:

Human performance is at the forefront of safety improvements. Complex-system industries have seen an increase in the proportion of injuries due to human error. This is best exemplified by the aerospace industry, where human factors component was the primary causal factor of 66% of fatal accidents, as opposed to 7% for aircraft-related factor. Recent accidents involving 737 MAX and SW1380 aircraft showed that human error is exacerbated during complex failure scenarios, characterized by multiple alarm messages, startling reaction and compounding sequence of errors. Human factors engineering has a clear role to identify proactively which aspects of the work environment are error-prone based on human capacities, and to propose design improvements to fix the issues before an error happens. This is the realm of prospective error analysis. However, existing methods assume a deterministic human behaviour, whereas there is a diversity of problem-solving abilities under stress. They also lack guidance and usability to be used for complex failure scenarios, as the situation evolves into multiple outcomes; yet this is where they would be the most beneficial to improve safety.

In this thesis project, you will study new approaches based on error analysis of the joint interaction between pilots and automated systems. To this end, you will develop a prospective error analysis method based on complex failure scenarios. You will use cases drawn from literature or accidents, such as the 737 MAX. You will improve the usability of the method to ensure its adoption by the human factors engineering community. Finally, you will carry out comparative studies to measure the performance of our new method with existing error analysis tools.

Workplan:

You will be enrolled in the doctoral program in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, under the supervision of Professor Philippe Doyon-Poulin. You will be remunerated by Polytechnique Montréal via Professor Doyon-Poulin’s grant.

You will be required to complete the academic requirements of the doctoral program (15 credits), and complete the research project.

You will need to be present in Montreal for the coursework and the majority of the research.

Note that Polytechnique Montreal is a French-speaking school and classes are offered in French.

Research team:

You’ll be joining Professor Philippe Doyon-Poulin’s Human Factors Engineering research group, which includes other doctoral, master’s and post-doctoral research students. You can consult the group’s recent publications for an overview of the subjects studied by other students https://www.polymtl.ca/expertises/en/doyon-poulin-philippe

You’ll also have the opportunity to take private pilot ground school to learn more about aviation.

Requirements:

• Training in ergonomics, human factors or usability.
• Knowledge of aviation and cockpits is an asset.
• Fluency in English spoken and written to be able to interact in a work environment and to read and write scientific articles. Fluency in French is an asset, as classes are offered in French.
• Demonstrate the following qualities: autonomy, resourcefulness, perseverance and respect for deadlines.

Master and PhD Positions

TOPIC 5: AUTOMATED SOFTWARE AUDITING FOR SMART CONTRACTS

TOPIC 6: INTENTION DRIVEN DEVELOPMENT

Description – Project 1

CONDITIONS

Software Engineering Machine Learning NLP and LLM Search Based Software Engineering Empirical Software Engineering

DETAILED DESCRIPTION

We are not aiming to replace auditors but to

• provide them with support through audit suggestions and code
• leverage code similarity and code transformation approaches and utilize previous audit reports for detecting security issues in new, unaudited smart contracts and correcting these issues.
• Develop State of the art binary analysis techniques for cross-platform code analysis
• Develop new security auditing assistance tools for smart contracts

Description – Project 2

CONDITIONS

Software Engineering Model Driven Software Engineering Machine Learning NLP and LLM Search Based Software Engineering

DETAILED DESCRIPTION

The project aims at devising an intelligent new generation of software development frameworks for emerging computing platforms (eg, cloud, blockchain) that aims to make programming accessible to citizen developers (business users with little development experience). Particularly we will develop novel model synthesis and conceptualization approaches and tools by leveraging language models and harnessing human-in-the-loop feedback to enable citizen developers to organize and translate their ideas into low-code (models) despite their lack of familiarity with the syntax of the low-code language, an approach I call Intention Driven Development (IDD).

TOPIC 7: TOWARDS A GREENER CONSTRUCTION INDUSTRY: A STUDY OF GREEN BEHAVIORS

DETAILED DESCRIPTION:

Project description

Still too polluting and energy-intensive, the construction industry represents a real challenge for the environment due to its greenhouse gas emissions and the millions of tons of residue caused by demolition as well as the massive use of water on construction sites. To reduce these environmental impacts, one of the most promising avenues is research into eco-responsible behavior. This project aims to analyze the levers and obstacles to the adoption of these behaviors. It will pave the way for the integration of eco-responsible behaviors ranging, for example, from recycling on construction sites to the reduction of materials at source in order to respond more effectively to the problem that an ecological transition may represent in the construction industry. . For this project, a field survey will have to be carried out (interviews and questionnaires).

Required profile

• Inclusive excellence. Excellence is not defined only quantitatively (ie by academic results). I prioritize a notion of excellence that encourages diversity in the ways of creating and transmitting knowledge.
• Knowledge of environmental management
• Knowledge of the construction industry (an asset)
• Skills in qualitative (interview, focus groups) and quantitative (questionnaire) methods. Otherwise university courses will have to be taken in methodology
• Excellent quality of communication and writing in French (project team meetings with the partner take place in French)
• Good command of English (scientific articles are generally in this language)
• Motivation, work ethic, open-mindedness, intellectual curiosity, creativity, collaborative spirit
• Interest in mobilizing knowledge outside of academia
• Interested in becoming an actor of change, respectful of our ecosystems, because the next engineers and managers will have to position humans and the environment at the heart of organizational decisions.

Please send your CV, transcript and cover letter: virginie.francoeur@polymtl.ca

TOPIC 8: Numerical modeling of cavitation near solid objects (PhD and MSc)

CONDITIONS:

The successful candidates have:

• A university degree in mechanical engineering, aerospace engineering, chemical engineering, or a related discipline.
• A solid background in fluid and solid dynamics, thermodynamics and numerical modeling.
• A proactive, team-oriented and curiosity-driven work attitude.
• Programming experience, preferably in C/C++, related to numerical modeling.
• Excellent written and verbal communication skills in English.
• French skills are a benefit.

The successful candidates will commence their studies in the fall trimester 2024 (starting late August 2024) or winter trimester 2025 (starting January 2025).

DETAILED DESCRIPTION:

Cavitation-assisted or cavitation-driven applications are rapidly emerging in a wide range of industrial sectors, from chemical and power engineering to materials science and medicine. These applications leverage the strong energy focusing driven by cavitation, e.g. temperatures hotter than the surface of the sun, to bring about a desired physical effect. Close to an object, a cavitation bubble collapses asymmetrically and is pierced by a fast liquid jet, called jetting cavitation, which can reach well over 100 m/s and generate exceptionally large shear stresses upon impact on a solid surface. Using lasers or focused ultrasound, this jetting cavitation can be applied with micrometer precision and, thus, facilitate new technologies, such as the precision peening of metals or the piercing of biological membranes for targeted drug delivery. However, jetting cavitation may, for example, also cause traumatic brain injury and erode surfaces. Despite significant progress in understanding cavitation, harnessing the strong energy focusing produced by jetting cavitation for engineering applications remains challenging.

I’m looking for PhD and MSc (research) students to join me in the Department of Mechanical Engineering at Polytechnique Montréal to study the dynamics of cavitation bubbles in the vicinity of solid objects, and to use and develop state-of-the-art numerical modeling tools. This project builds directly on our recent discovery that jetting cavitation dynamics can be manipulated by applying an ambient flow around the bubble. As part of this project, we will be working closely with our collaborators in Canada and abroad. The long-term aim of this project is to enable the precise and controllable application of jetting cavitation in emerging technologies. To this end, the PhD and MSc students will:

• Develop new finite-volume methods for predicting the complex fluid-structure interactions between cavitation-driven flows and nearby solids.
• Develop computational tools for processing, identifying and classifying acoustic signals originating from cavitation, leveraging modern signal processing and machine learning techniques.
• Conduct numerical simulations of cavitation bubble dynamics in the vicinity of solid objects, using high-performance computing systems.
• Analyze and quantify the pressure and shear exerted on the surface during liquid jet impact, as well as the acoustic emissions generated by bubble collapse and jet impact.
• Cooperate with our collaborators in Canada and abroad.

Recent publications related to this research project are, for instance:

• Mnich, Reuter, Denner & Ohl, Single cavitation bubble dynamics in a stagnation flow. Journal of Fluid Mechanics 979 (2024), A18.
• Denner & Schenke, Modeling acoustic emissions and shock formation of cavitation bubbles. Physics of Fluids 35 (2023), 012114.
• Denner, Xiao & van Wachem, Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation. Journal of Computational Physics 367 (2018), 192-234.

TOPIC 9: REALTIME COMPENSATION OF MAGNETIC FIELD VARIATION IN MRI

TOPIC 10: PROCESSING OF MRI DATA FROM THE BRAIN AND SPINAL CORD

DETAILED DESCRIPTION – PROJECT 1:

• To obtain a nice image inside the body, an MRI device assumes that the main magnetic field (B0) is homogeneous and stable over time. Unfortunately (or fortunately for the subject), subjects breathe while in the scanner. When subjects breathe, the B0 field varies in time and space. The NeuroPoly laboratory, in collaboration with Dr. Stockmann (Harvard/MIT), has developed hardware and software methods to compensate for these variations. The principle is based on the development of so-called “shim” coils, in which a direct current circulates, generating a magnetic field (Biot-Savart law) which compensates for the inhomogeneities of the B0 field. To orchestrate these complex experiments, the NeuroPoly laboratory has also developed free software: https://shimming-toolbox.org. We recruit Master/PhD/Postdoc students to work on real-time shimming projects. Examples of projects: implementation of optimization methods to find the right shim current combinations for each coil element (taking into account physical constraints, such as the maximum allowed current and dB/dt), experimentation with human subjects at 3T and 7T (taking measurements, image analysis), comparison and implementation of robust methods to map the B0 field, dynamic and real-time shimming of the spinal cord at 7T. The research will be conducted at the NeuroPoly laboratory (Polytechnique, Université de Montréal, www.neuro.polymtl.ca), at the Montreal Neurological Institute (MNI, McGill University) and in collaboration with the Martinos Center (Harvard/MIT).
• Skills: Ultra-high field MRI | MRI acquisition | Image analysis

DETAILED DESCRIPTION – PROJECT 2:

MRI is routinely used in clinics to detect certain diseases such as multiple sclerosis or stroke. However, clinical MRI is limited due to its low sensitivity for detecting abnormalities in the white matter (axons). New MRI techniques now allow characterization of myelin from axons, and therefore provide a better diagnosis. However, these techniques require particular image processing before they can be interpreted by radiologists. The goal of this research theme is to develop new methods that can then be used clinically to quantify abnormalities of axons in the brain and spinal cord. Examples of projects:

• Set up an analysis pipeline to automatically process MRI scans, perform quality control of the analysed datasets (eg: identify failed automatic processes and find solutions);
• Interpret the results with the help of neurologists, perform statistics to find best biomarkers from MRI data (eg: spinal cord cross-sectional area, MS lesion load).
• Create MRI templates, representing the average anatomy across several individuals.

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