Artificial intelligenceMachine learningFall 2027Emailing professors

How to Email AI and Machine Learning Professors for Fall 2027 (50 Professors in Canada and Europe)

Emad Deilam SalehiPhD CandidateOct 24, 2026 · 8 min read

If you want to start a PhD or research master's in AI or machine learning in Fall 2027, the professors you contact in the next few weeks will shape where you end up. This guide covers when to write, how to choose who to write to, and a list of 50 AI and machine learning professors in Canada and Europe to start from.

When to email for a Fall 2027 start

Many funded PhD programs in Canada and the United Kingdom set their Fall 2027 application deadlines between December 2026 and February 2027, and a professor who wants to support your application usually needs to hear from you well before that. In much of continental Europe, PhD places are often advertised as individual jobs with their own closing dates, so it pays to write early and keep checking for new postings.

For most applicants, that means sending your first emails between late October and early December 2026. Our guide on when to contact professors for Fall 2027 walks through the full timeline, and how to contact professors in Europe versus North America explains why the email itself should differ by region.

How to use this list

Filter by research first. Open the homepage of anyone whose research focus overlaps with yours and read their two or three most recent papers. Skip anyone whose work you could not discuss in a sentence or two.
Look for a recruiting signal. A prospective students page, an openings section or a recent post about funded positions is a good sign. Our guide on checking whether a professor is accepting students explains what to look for.
Write one email per professor. Name one specific paper or project and say what you would contribute to it. A clear subject line decides whether it gets opened at all.
Follow the official route. Even after a positive reply, you usually still need to apply through the university before its deadline.

The 50 professors, by country

Every homepage below was checked on October 24, 2026. A listing here is not a promise of an opening, so treat the professor's own page as the final word. Prefer a table you can scan side by side? The same professors are in our AI and machine learning open positions directory.

Canada (27)

Golnoosh Farnadi (Assistant Professor, McGill University, Montreal): Trustworthy machine learning; fairness, privacy, robustness, explainability; responsible AI and multi-agent systems
Jun Jin (Assistant Professor, University of Alberta, Edmonton): Machine learning, reinforcement learning and robotics; human-centered autonomy
Majid Komeili (Associate Professor, Carleton University, Ottawa): Applied machine learning, computer vision, natural language processing, multimodal and vision-language models, agentic AI, interpretability and robustness
Ji Hyun Ko (Associate Professor, University of Manitoba, Winnipeg): Medical imaging and AI: imaging-based biomarkers, MRI/PET brain imaging, machine-learning-based multimodal brain imaging analysis, neurological disorders and neurostimulation
Jun Chen (Professor, McMaster University, Hamilton): Machine learning, NLP, computer vision, learned image/video compression and restoration, large-language-model compression and wireless communications
Jun Yan (Associate Professor, Concordia University, Montreal): Artificial intelligence, machine learning, agentic/autonomous systems, digital twinning, cyber-physical systems, smart infrastructure, energy resilience and anomaly management
Parvaneh Saeedi (Professor, Simon Fraser University, Burnaby): Artificial intelligence, computer vision, medical image analysis, machine learning for healthcare/biomedical imaging and geospatial applications
Shabnam Jabari (Associate Professor, University of New Brunswick, Fredericton): Applied geospatial AI, computer vision, deep learning and foundation models for digital twins, urban climate resilience and disaster mapping
Louis Petit (Assistant Professor, Université de Sherbrooke, Sherbrooke): Field robotics, autonomous navigation, multimodal perception, 3D mapping, embedded/self-supervised learning
Russell Dinnage (Assistant Professor, University of Alberta, Edmonton): AI foundation models for ecology, species-distribution modelling, computational phenomics and organismal biology
Ryan Ahmed (Assistant Professor, McMaster University, Hamilton): Artificial intelligence, machine learning and deep learning applied to autonomous vehicles, electric/hybrid powertrains, energy systems and smart systems
Hubert Kenfack Ngankam (Assistant Professor, Université de Sherbrooke, Sherbrooke): Ambient intelligent systems, multimodal generative AI/LLMs, machine learning, computer vision, smart habitats and health assistance
Julien Sylvestre (Professor, Université de Sherbrooke, Sherbrooke): AI engines on optical computers; machine learning, optoelectronics, optical computing, automation and data mining
Lei Zhang (Associate Professor, University of Regina, Regina): Artificial intelligence, machine learning, artificial neural networks, spiking neural networks, cognitive computing, hardware acceleration, ultrasound and pipeline inspection
S. Andrew Gadsden (Associate Professor, McMaster University, Hamilton): Artificial intelligence and machine learning for cognitive/smart systems, perception-action, sensing and autonomous applications
Saiedeh Razavi (Associate Professor; Chair in Heavy Construction, McMaster University, Hamilton): Artificial intelligence and machine learning applied to connected/autonomous vehicles, smart construction, transportation, infrastructure, safety and mobility
Yuhong He (Professor, University of Toronto Mississauga, Mississauga): Applied AI and machine learning for remote sensing, precision agriculture, vegetation stress and urban ecosystem monitoring
Ji Won Suh (Assistant Professor, University of Victoria, Victoria): Remote sensing, GeoAI, time-series analysis, anthropogenic-impact monitoring, primary forest mapping and biodiversity mapping
Marzena Karpinska (Assistant Professor, Simon Fraser University, Burnaby): Natural language processing, language models, multilingual NLP, machine translation and long-form text processing
Stephen L. Smith (Professor; Canada Research Chair, University of Waterloo, Waterloo): Artificial intelligence, machine learning, robotics, autonomous systems, human-robot interaction, multi-robot systems and intelligent transportation
Steven Livingstone (Associate Professor, Ontario Tech University, Oshawa): Affective data science; machine learning for recognizing, predicting and expressing emotion; physiological and motion-capture data
Dongmei Chen (Professor, Queen's University, Kingston): Machine learning and AI-based methods for change detection from time-series remotely sensed data
Hina Shaheen (Assistant Professor, University of Manitoba, Winnipeg): AI in neuroscience, machine learning, computational modelling, biostatistics, clinical trials and high-dimensional data analysis
François Leduc-Primeau (Associate Professor, Polytechnique Montréal, Montreal): Energy-efficient deep learning, machine learning for communication systems, digital hardware and telecommunications
Jocelyn Qiaochu Chen (Assistant Professor, University of Alberta, Edmonton): Program synthesis, neurosymbolic programming, LLM-assisted programming and reasoning
Afsoon Soudi (Assistant Professor, Toronto Metropolitan University, Toronto): Responsible machine learning and artificial intelligence; audience analytics; computational social science; climate communication and social-media sentiment analysis
Evan Shelhamer (Assistant Professor, University of British Columbia, Vancouver): Machine learning, computer vision and visual recognition

United Kingdom (12)

Ajitha Rajan (Professor (Personal Chair of Software Testing and Verification), University of Edinburgh, Edinburgh): Biomedical AI and explainability for NLP/CV; AI compiler testing; autonomous-vehicle software testing
Gerardo Aragon Camarasa (Senior Lecturer, University of Glasgow, Glasgow): Applied robotics and AI for advanced manufacturing; robotic perception/manipulation; machine learning; computer vision; autonomous systems
Vinod Kumar Chauhan (Strathclyde Chancellor’s Fellow (Lecturer/Assistant Professor), University of Strathclyde, Glasgow): Causal AI, machine learning for personalised treatment and healthcare analytics
Aladdin Ayesh (Personal Chair of Artificial Intelligence (Professor), University of Aberdeen, Aberdeen): Applied AI, machine learning, explainable AI, computational cognition, health informatics, sustainable development and data privacy
Christos Tachtatzis (Professor, University of Strathclyde, Glasgow): Applied artificial intelligence and industrial informatics; deep-learning image/video analytics; anomaly detection; remote sensing and computer vision
Francesco Muschitiello (Associate Professor, University of Cambridge, Cambridge): Machine learning and Bayesian methods for climate and environmental science; climate-model evaluation and prediction
Georgios Leontidis (Professor of Machine Learning, University of Aberdeen, Aberdeen): Machine learning and deep learning applied to environmental data, food/agri-food systems, nuclear reactors, industrial forecasting, computer vision and AI
Simos Gerasimou (Senior Lecturer, University of York, York): Trustworthy robotics and autonomous systems; safe AI; reinforcement learning; decentralised planning
Jason Hartford (Dame Kathleen Ollerenshaw Fellow, Machine Learning and Robotics, The University of Manchester, Manchester): Causal representation learning; causal inference from unstructured data; multimodal representation learning; AI in science
Michele Sevegnani (Reader in Computing Science, University of Glasgow, Glasgow): Responsible use of LLM-based tooling in high-stakes decision-making; trustworthy AI and formal verification
Junqi (Billy) Tang (Assistant Professor in Mathematical Optimisation and Data Science, University of Birmingham, Birmingham): Machine learning, learning-to-optimise/data-driven optimisation, deep learning for computational imaging and data science
Sabina Sloman (Assistant Professor in Statistics and Data Science, University of Birmingham, Birmingham): Bayesian inference, probabilistic machine learning, Bayesian experimental design, transfer learning, inverse problems, uncertainty quantification and scientific applications

Germany (3)

André Platzer (Alexander von Humboldt Professor, Karlsruhe Institute of Technology (KIT), Karlsruhe): Logic of autonomous dynamical systems; provably safe AI; cyber-physical systems, autonomous cars, aerospace, robotics
Matthias Hein (Bosch Endowed Professor of Machine Learning, University of Tübingen, Tübingen): Machine learning; applied machine learning and computer vision
Anke Schmeink (Professor, RWTH Aachen University, Aachen): Data analytics and machine learning applied to climate-neutral/resource-efficient building and engineering systems

Switzerland (3)

Robert Katzschmann (Assistant Professor of Robotics, ETH Zurich, Zurich): Soft robotics; robot learning; dexterous manipulation; embodied AI; clinical bioprinting
Marco Hutter (Full Professor, ETH Zurich, Zurich): Legged and mobile robotics; robot learning; reinforcement learning for robot control; perception and navigation
Alexander Mathis (Associate Professor, École polytechnique fédérale de Lausanne (EPFL), Geneva): AI4Science; sensorimotor learning; computer vision; robotics; computational neuroscience; wildlife-conservation applications

Portugal (2)

Jaime S. Cardoso (Full Professor, University of Porto (FEUP), Porto): Computer vision, machine learning, medical decision support systems, medical-image analysis
José Santos-Victor (Full Professor, Universidade de Lisboa, Instituto Superior Técnico, Lisbon): Computer vision, robot vision, machine learning, cognitive robotics, learning and adaptation

Austria (1)

Nils Morten Kriege (Associate Professor, University of Vienna, Vienna): Machine learning with graphs; structured-data learning; graph algorithms and cheminformatics

Denmark (1)

Christina Lioma (Professor, University of Copenhagen, Copenhagen): Applied machine learning, NLP, information retrieval, text analytics, recommender systems, AI

Spain (1)

Jesús Mayor Márquez (Profesor Permanente Laboral, Universidad Politécnica de Madrid, Madrid): Artificial intelligence, virtual reality, computer graphics and data science

Make the fit obvious

Professors in AI and machine learning receive a lot of email, and the ones that get replies make the connection easy to see: this is the problem you work on, this is what I have done that is close to it, and this is the question I would like to explore with you. Our guide to matching your interests helps you put that into words.