Current Graduate Opportunities

PhD position: Computational investigation of electrocatalysts for the ammonia oxidation reaction – Prof. Leanne Chen

The ammonia electrooxidation reaction (AOR) has potential applications for energy generation and as an alternative to current energy intensive wastewater treatment methods. Current research on AOR has been geared towards finding higher performing and more cost-effective catalysts. Thus far, most experimental and computational AOR studies have focused on platinum-based catalysts; however, recently studies involving nickel-based catalysts have been increasing in number due to nickel’s lower cost. We have previously established the lowest energy intermediates and the minimum energy pathways of AOR for the formation of nitrate, nitrite, and dinitrogen on the (0001) facet of β-Ni(OH)2 using density functional theory, however, pure Ni(OH)2 has a high overpotential for the AOR. Thus, the current research goal is to identify suitable transition-metal candidates to dope into pure Ni(OH)2 that potentially decrease the overpotential for this reaction. 
Start date: Immediately.

MSc position: Selective reaction of sulfenate anions – Prof. Adrian Schwan

The Schwan group has been studying the influence of proximal groups to direct sulfenate (R-S-O-M+) S-alkylation chemistry through non-bonding interactions. For example, the interaction of the nitrogen, oxygen and hydrogen components of a nearby NHBoc group with sulfenate oxygen and counterion can direct stereoselective alkylations. Similarly, a proximal boron grants an opportunity for accelerated, site-selective S-functionalization. The MSc project will further explore some of these interactions.
Start date: May or September 2024.

MSc & PhD positions: Machine Learning on High-Dimensional Biological Data Analysis – Prof. Yan Yan

My research area is bioinformatics, and we mainly use machine learning and statistical methods to handle large scale high-dimensional biological data. Projects include penalized regressions and deep learning on ingle-cell RNA sequencing (scRNA-seq), feature selection on whole-genome Single Nucleotide Polymorphisms (SNPs) data, and predictive models for phenotype predictions based on phenotype data. A major focus of the research is on plants data and agriculture applications, but we also investigate other types of applications. 
Start date: Jan. 2024 or Sept. 2024.

Post-doc position: Building a Surveillance tool for infectious disease (i.e. Avian Influenza) prevention and monitoring – Prof. Rozita Dara

One full-time post-doctorate position in the area of infectious disease monitoring and modeling is available at the Data Management and Privacy Governance Lab. The research program is an interdisciplinary program focused on developing new methods and solutions for infectious disease surveillance. The successful candidate will collaborate with other postdoctoral fellows and graduate students to design and develop novel algorithms, decision support systems, metrics, best practices, and protocols for infectious disease modeling. 
Start date: January 2024 to August 2024 (2 year position).

PhD position: Using machine learning to enhance privacy and security ensuring solutions – Prof. Rozita Dara

One PhD position in the area of privacy and/or security enhancing technologies is available at the Data Management and Privacy Governance Lab. The research program is an interdisciplinary program focused on developing new methods and solutions to enhance privacy and security of personal data in IoT ecosystem, smart-farming, and other related fields. The successful candidate will collaborate with other postdoctoral fellows and graduate students to design and develop novel artificial intelligence algorithms and approaches, decision support systems, metrics, best practices, and protocols for data privacy and security protection. 
Start date: Fall 2024 or Winter 2024.

PhD position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a PhD student to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have publication experience, with international conferences such as NeurIPS, ICML, ICLR and CVPR or their associated workshops a strong asset. You are keen to raise awareness of biodiversity research in those communities. You have experience building ML models with Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration.
Start date: September 2024

Post-doc position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a postdoctoral fellow to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have a strong publication record, preferably in international conferences such as NeurIPS, ICML, ICLR and CVPR. You are keen to raise awareness of biodiversity research in those communities. You have mastered Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration.
Start date: September 2024

PhD position: A Virtual Care Navigator for a Healthier Community – Prof. Yan Yan

We are looking for a PhD student to fill a position in collaboration with esteemed external partners. Dive into the development of Virtual Reality (VR) and Artificial Intelligence (AI) solutions aimed at enhancing hospital patient experiences. This project aims to revolutionize the way healthcare is delivered and experienced. The aim is to create a Virtual Care Navigator (VCN), a pioneering digital platform designed to connect the Algoma District community with vital mental health resources efficiently. By merging psychosocial insights, community-informed research, artificial intelligence (AI), and bioinformatics, the VCN will serve as a dynamic tool to facilitate access to up-to-date mental health support tailored to individual user profiles. This project, championed by an interdisciplinary team of experts, seeks to alleviate the strain on local healthcare systems and enhance the accessibility and delivery of mental health care services.

If you're interested in applying for the position, please prepare the following: A) A brief statement of interest, indicating the project you wish to contribute to and why, and specify when you want the program to start; B) An overview of your relevant skills and experiences; C) Your CV and an unofficial transcript. Please submit your application to yyan15@uoguelph.ca with subject [PHD F24/W25 – Student Name – Project Title] by Apr 30, 2024

Start date: Sept 2024 or Jan 2025

PhD position: Privacy-aware Student Wellness Monitoring System – Prof. Yan Yan

We are looking for a PhD student to fill a position in collaboration with esteemed external partners. Participate in creating a digital platform that connects community members with mental health resources efficiently. This project focuses on utilizing AI and bioinformatics to tailor mental health support to individual needs, significantly impacting community well-being. The Privacy-aware Student Wellness Monitoring System is a project aimed at early identification of mental health and social challenges among university students. By harnessing the collective expertise of faculty from the School of Computer Science and Technology, the School of Social Work, and the Department of Psychology, this system will monitor various aspects of student well-being, including mental and physical health, housing affordability, food security, and social support. Designed to maintain the highest standards of privacy and confidentiality, this system will provide real-time, actionable data to facilitate proactive interventions for students at risk.

If you're interested in applying for the position, please prepare the following: A) A brief statement of interest, indicating the project you wish to contribute to and why, and specify when you want the program to start; B) An overview of your relevant skills and experiences; C) Your CV and an unofficial transcript. Please submit your application to yyan15@uoguelph.ca with subject [PHD F24/W25 – Student Name – Project Title] by Apr 30, 2024

Start date: Sept 2024 or Jan 2025

PhD position: Patient-Centric Healthcare using VR and AI – Prof. Yan Yan

We are looking for a PhD student to fill a position in collaboration with esteemed external partners. Contribute to the development of a secure, AI-driven system for monitoring and supporting student mental health. This initiative emphasizes privacy and early intervention, aiming to normalize mental health care in the academic setting. Our research project embarks on a transformative journey to redefine patient care through the integration of Virtual Reality (VR) and Artificial Intelligence (AI). By leveraging the immersive capabilities of VR coupled with the analytical power of AI, we aim to enhance the hospital experience for patients, making it more intuitive, comforting, and responsive to their needs.

If you're interested in applying for the position, please prepare the following: A) A brief statement of interest, indicating the project you wish to contribute to and why, and specify when you want the program to start; B) An overview of your relevant skills and experiences; C) Your CV and an unofficial transcript. Please submit your application to yyan15@uoguelph.ca with subject [PHD F24/W25 – Student Name – Project Title] by Apr 30, 2024

Start date: Sept 2024 or Jan 2025

M.Sc. & PhD positions: AI+Health research in the AI Lab - Prof. Ed Sykes

We invite MSc and PhD students to join the AI Lab at the School of Computer Science to pursue groundbreaking research in Artificial Intelligence and Machine Learning within the realm of health innovation. The AI Lab focuses on advancing health informatics, predictive analytics, and personalized healthcare solutions through collaborations with academic institutions, industry partners, and healthcare providers. Our mission is to develop AI-driven innovations that improve patient outcomes and healthcare delivery, with research spanning the following areas: Chronic disease, aging population and elder care, and healthcare accessibility and resources.

Start date: January 2025

More information and requirements for the AI+Health opportunity

Postdoctoral Scholar Position in the AI Lab at the University of Guelph - Prof. Ed Sykes

The AI Lab in the School of Computer Science at the University of Guelph invites applications for Postdoctoral Scholars to carry out exciting, industry-focused research and development in Artificial Intelligence, Machine Learning, and Deep Learning (AI/ML/DL) in the area of Health and/or mHealth. Candidates for this position should hold a Ph.D. degree in Computer Science or Software Engineering and will have completed their Ph.D. within the past 2-3 years. They should demonstrate experience in AI/ML/DL using Python, TensorFlow, PyTorch, etc. The Post Doc is expected to work collaboratively with McMaster University and the McMaster Centre for Software Certification (McSCert).

More information and requirements for the AI+Health opportunity

MASc position: Understanding groundwater, agronomy and drainage system design influences on tile water quantity and quality – Prof. Jana Levison

The goal of this research is to examine how tile drainage design influences groundwater quantity and quality in agricultural fields. The research will examine how spatiotemporal variations in groundwater recharge, land cover crop, geology, and nutrient application influence tile drainage performance. The proposed research will be conducted at the Huronview Demonstration Farm located near Clinton, Ontario. This project is in partnership with the Ausable Bayfield Conservation Authority (ABCA), Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), Huron County Soil and Crop Improvement Association (HCSCIA) and the Land Improvement Contractors of Ontario (LICO). 
Start date: January or May 2024

Learn more about the M.A.Sc position

MASc position: Assessment of fracture network geometry, anisotropy and preferential flow directions in a bedrock aquifer – Prof. Jonathan Munn

This MASc project is part of an NSERC Alliance Grant studying the groundwater flow system around a major bedrock quarry in the City of Guelph (Dolime Quarry). It includes detailed fracture network geometry assessment using surface and borehole methods (scanlines, drones, geophysics). Hydraulic head data from multilevel wells and pumping test data from municipal supply wells will be used to assess aquifer anisotropy, providing a more robust understanding of the local hydrogeological conditions.
Start date: 2024 (January, May or September)

MASc position: Integrating values and social justice outcomes in STEM education and Engineering Design – Prof. Julie Vale

Including values-based learning and assessment strategies can empower students to better engage with their complex worlds. Mindsets within Science Technology Engineering and Math (STEM) students and practitioners such as the culture of disengagement, meritocracy, and the neutrality problem have been identified as barriers to engaging with values-based learning experiences. This project adopts an architectural analogy to vertical curriculum integration: cornerstone, keystone, and capstone, and examines how vertical integration of values-based outcomes in STEM curricula can better instill those values in STEM students. Data analysis will involve a combination of meta-analysis of existing data and mixed methods of new data to investigate student and faculty perceptions of values-based outcomes, interventions, and pedagogies in a variety of levels of STEM courses. The results will be shared widely with the larger research community and will enable the development of workshops to assist STEM educators and administrators to mindfully integrate the values they wish to instill. 
Start date: May 2024

MASc or PhD position: Separation of miscible solvents from water – Prof. Erica Pensini

We are looking for a MASc or PhD student to investigate the mixing behaviour between water and water miscible pollutants, in the context of water purification and pollutant transport in contaminated aquifers. Specifically, we have been analyzing the separation mechanisms between water and water-miscible toxic organic solvents, upon addition of amphiphiles, in the context of water purification. Understanding the mechanisms is important because it allows us to predict separation and optimize it, to ensure that it is fast and that the water phase has high purity. We are also interested in the separation mechanisms in the presence of salts (e.g., ions naturally present in groundwater), because they affect the migration of toxic miscible solvents in impacted aquifers. Typically, pollutants that mix with water travel faster than pollutants that are separated from water (say hydrocarbons). As a result, they pose greater risks to downstream receptors, such as drinking wells. 
Start date: Flexible

MASc or PhD position: AI and product development – Prof. Sheng Yang

One MASc/PhD position is available immediately in the Design Innovation and Intelligent Manufacturing (DIIM) lab. The DIIM lab is led by Prof. Sheng Yang based in the School of Engineering. Our goal is to realize design intelligence through enabling technologies such as Internet of Things (IoT), machine learning, digital twin, and extended reality. The research project aims to investigate the frontier research on human-AI teaming in product development utilizing various digital technologies such as VR, AR, LLM (large language models), and digital twin. Background in Mechanical/Mechatronic/Computer/Electric Engineering are preferred. However, exceptional candidates from other fields can also apply.

PhD position: Chemical Upcycling of Waste Plastics – Prof. Animesh Dutta

Our focus will be on pyrolysis of Post Consumer Plastic Waste (PCW) followed by their catalytic upgrading with zeolite-based composites. The activities are i) to combine plastics, biomass, and municipal waste (MW) into a catalytic pyrolysis process for the extraction of valuable chemicals specifically aromatics and olefins, to help understand the synergistic effect among the feedstocks; ii) to study pyrolysis of plastic wastes in different temperature zones to understand the reaction mechanisms of how products are formed; iii) to conduct model-free kinetic studies; and iv) to explore oxo-degradation pyrolysis for autothermal operation.
Start date: Immediately

PhD position: Integrated circuit design with focus on micro-power converters for battery-light IoT applications – Prof. Stefano Gregori

The on-going miniaturization of electronic circuits leads to computing, communication and sensing functions concentrated into tiny elements integrated seamlessly into the objects around us, which in turn can become smart and interconnected forming a widespread Internet of things (IoT). Often these circuits have limited resources in terms of available energy, computing power, and communication bandwidth. Therefore, resource efficiency is crucial to developing applications that can be scaled up to fulfil the promise of a smart world. One of the goals of my research group is to create new micro-power converters integrated onto a silicon chip containing other functions. Research students with a microelectronics background who are highly motivated and eager to explore unconventional ideas, and familiar with integrated-circuit technology, electronic-design-automation tools and electronic instrumentation should contact: sgregori@uoguelph.ca.
Start date: 2024 (January, May or September)

PhD position: Printed electronics with focus on green electronic systems for sensing and micro-generation – Prof. Stefano Gregori

The prospects of mass productions of electronics printed on green and biodegradable substrates can be enabled by the development of material printing techniques for the additive manufacturing of organic electronic devices and interconnections. One of the goals of my research group is to create new systems on foil based on non-conventional materials, like bio-carbons from co-products of biomass conversion. The idea is to incorporate bio-carbon and natural fibre hybrids into plastics for engineering green composites used in smart auto-parts, packaging and consumer products with embedded electronic functions. Research students with electronics and materials science background are needed to work on material printing techniques for producing conductive and insulating patterns, active devices, and hybrid integration systems. The new structures will implement temperature and shock sensor arrays, micro-generators, energy storage and management and form the basis for new sustainable applications, including smart food packaging, wearable patches, and integrated systems for monitoring batteries of autonomous systems and electric vehicles. If interested, please contact: sgregori@uoguelph.ca
Start date: 2024 (January, May or September)

PhD position: Developing Distributed Next Generation Network Systems – Prof. Ahmed Refaey Hussein

The research opportunity lies in the integration of zero-touch automation with zero-trust security models in Next-Generation Network Systems (NGNSs). While NGNSs excel in self-management and optimization through AI-driven mechanisms, they face challenges in meeting stringent security and legal requirements. Investigating how to seamlessly incorporate zero-trust frameworks into existing NGNS architecture could address these challenges, offering improved resilience, reliability, and legal compliance without compromising performance. This multi-disciplinary study will provide critical insights into scalable, secure, and legally compliant network systems. 
Start date: Immediately (4 year position).

PhD position: Deep Reinforcement Learning for Internet of Things – Prof. Lei Lei

Our research lab mainly focuses on the intelligent control, computation, and communications through machine learning, especially deep reinforcement learning. Our current research activities include: autonomous/artificial internet of things, dynamic energy management in Smart Grid, intelligent resource management in wireless communications, intelligent resource control in mobile cloud/edge/fog computing, autonomous driving, intelligent vehicular networking, vehicular edge/fog computing, and autonomous/networked/cloud robots.
Start date: May 2024 (3-4 year position).
For more information, visit Deep Intelligence Lab

PhD position: Sustainable Bioproducts for Automotives – Prof. Manjusri Misra

The project focuses on utilizing agro and forestry waste resources to develop sustainable electric car parts with flame retardancy. Start date: January 2024 (4 year position).

PhD position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a PhD student to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have publication experience, with international conferences such as NeurIPS, ICML, ICLR and CVPR or their associated workshops a strong asset. You are keen to raise awareness of biodiversity research in those communities. You have experience building ML models with Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration. Start date: September 2024

PhD position: Bioprocess engineering approaches for carboxylate production from waste streams – Prof. Guneet Kaur

Organic waste streams have huge chemical, energy and material potential due to the functionalized molecules stored in it. This can be converted to green chemicals using bioprocess engineering approaches. This project aims to investigate and develop upstream and downstream processing methods for value addition of waste/by-products streams from agri-food sector to carboxylates, high-value building block chemicals.

Post-doc position: Wet agri-food wastes and residues to hydrogen, new class fertilizers, and activated carbons – Prof. Animesh Dutta

There are three activities. Expanding innovative ideas with the integration of hydrothermal carbonization (HTC) and steam-based Calcium Looping Gasification (CLG) of hydrochar to handle wet agri-food wastes toward generating hydrogen and synthetic fertilizers; Developing innovative hybrid technologies utilizing supercritical water gasification (SCWG) to valorize the aqueous organic-rich solution produced during the HTC of wet agri-food wastes into hydrogen; and Designing innovative process to convert hydrochar into high-surface area activated carbons for different applications, i.e., hydrogen storage, and water/air treatments.
Start date: Immediately

Post-doc position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a postdoctoral fellow to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have a strong publication record, preferably in international conferences such as NeurIPS, ICML, ICLR and CVPR. You are keen to raise awareness of biodiversity research in those communities. You have mastered Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration.
Start date: September 2024

PhD position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a PhD student to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have publication experience, with international conferences such as NeurIPS, ICML, ICLR and CVPR or their associated workshops a strong asset. You are keen to raise awareness of biodiversity research in those communities. You have experience building ML models with Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration.
Start date: September 2024

Post-doc position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a postdoctoral fellow to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have a strong publication record, preferably in international conferences such as NeurIPS, ICML, ICLR and CVPR. You are keen to raise awareness of biodiversity research in those communities. You have mastered Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration.
Start date: September 2024

MSc position: Rheology of Dispersions of Soft Nanoparticles – Dr. John Dutcher

Seeking qualified MSc candidates to work on the characterization of new nanomaterials based on phytoglycogen, a highly branched glucose polymer produced as compact, soft, hairy nanoparticles in sweet corn. The student will modify the nanoparticles with different functional groups and measure the effect on the interactions between particles at high packing fractions using a state-of-the-art rheometer.
Start date: January 2024 (2 year position).

• Rheology of Dispersions of Soft Nanoparticles graduate student position

MSc position: Deep Learning and Infrared Spectroscopy of Polymers - Dr. John Dutcher

Seeking qualified MSc candidates to work on the application of deep learning to the analysis of large databases of high-resolution infrared images of polymers, with the goal of understanding the degradation and failure mechanisms of polymers used in water transport applications. This work is at the forefront of the application of machine learning strategies to the analysis of large databases, an emerging area at the intersection of physical and data science.
Start date: January 2024 (2 year position).

• Deep Learning and Infrared Spectroscopy of Polymers graduate student position

MSc & PhD position: Protein NMR Research Group – Prof. Vladimir Ladizhansky

We are interested in membrane protein structure, dynamics and folding. We use solid state Nuclear Magnetic Resonance to map protein conformation and dynamics. 
Start date: Fall 2024

PhD position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a PhD student to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have publication experience, with international conferences such as NeurIPS, ICML, ICLR and CVPR or their associated workshops a strong asset. You are keen to raise awareness of biodiversity research in those communities. You have experience building ML models with Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration.
Start date: September 2024

Post-doc position: Machine Learning for Biodiversity – Prof. Graham Taylor

Seeking a postdoctoral fellow to be based in the Machine Learning Research Group and affiliated with the Vector Institute for Artificial Intelligence. You are motivated to advance AI/ML research in the service of BIOSCAN’s ambitious global mission. You will have the opportunity to work on projects that span computer vision and DNA sequence analysis. For vision, this involves pushing the limits of fine-grained recognition for taxonomic categorization using techniques such as self-supervised learning, generative models and sim2real. For DNA sequences, this involves graph representation learning to predict missing links and evolutionary paths from recovered structures. The data collected in BIOSCAN will also support learning joint visual-DNA representations. You have a strong publication record, preferably in international conferences such as NeurIPS, ICML, ICLR and CVPR. You are keen to raise awareness of biodiversity research in those communities. You have mastered Python-based frameworks such as PyTorch/TensorFlow/JAX. You also have experience managing experimental workflows on GPU-enabled clusters. You are open to and ideally experienced in cross-disciplinary collaboration.
Start date: September 2024