We invite applications for an in-person High Performance Computing and AI Research Enablement Fellow at the Trottier Space Institute at McGill (TSI). TSI is an interdisciplinary research centre within McGill University consisting of 17 affiliated faculty members from the Departments of Physics, Earth and Planetary Sciences, and Atmospheric and Oceanic Sciences at McGill. Research topics include (but are not limited to) astrophysics, cosmology, astrobiology, geophysics, life in extreme environments, and the search for extraterrestrial biosignatures.
This position will be appointed within the University's postdoctoral scholar category and will play a central role in supporting and advancing computational research across the TSI community. Our researchers rely extensively on advanced computational methods, high-performance computing (HPC), scientific software development, and increasingly, artificial intelligence (AI) tools to support cutting-edge discovery.
The successful candidate will serve as the Institute's primary resource for research computing, helping faculty members, postdoctoral scholars, graduate students, and other trainees make effective use of modern computational resources and practices. Working closely with the TSI community, the Fellow will provide expertise in HPC, scientific software development, GPU computing, code optimization, research software sustainability, and the responsible use of AI and large language models (LLMs) in research environments.
Deadline to Apply:
2026-11-11
To Apply:
Interested candidates should send a statement of interest (max 1 page), detailed CV, and the names and contact information of three potential referees (who will be contacted only if the candidate makes our short list), all bundled in one pdf and addressed to the TSI Program Administrator Dr. Alexandre David-Uraz at alexandre.david-uraz@mcgill.ca with subject line “TSI HPC Fellow”. Applications will be reviewed starting immediately and will continue until the position is filled.
Role Overview:
The Fellow's primary mandate is to strengthen computational capacity across TSI by enabling researchers and trainees to make productive and ethical use of LLMs for various aspects of computing, reducing technical barriers to research, promoting best practices in scientific computing, and enabling researchers and trainees to make effective use of national and institutional computational resources.
The Fellow's effort is expected to focus primarily on research computing consultation, AI enablement, and training activities, with approximately 20% of time available for independent or collaborative research aligned with TSI's scientific mission.
KEY RESPONSIBILITIES:
High-Performance Computing and Research Computing
- Advise researchers and trainees on the effective use of HPC resources, including systems provided through the Digital Research Alliance of Canada and CANFAR.
- Provide consultation and troubleshooting support for research computing challenges, including GPU computing, software optimization, and the effective use of HPC resources.
- Promote best practices in research software development, code documentation, testing, and sustainable computational workflows.
- Help researchers identify appropriate computational solutions for data-intensive and compute-intensive research projects.
AI and Large Language Model Enablement
- Guide researchers in the effective use of AI and LLM tools for software development, code documentation, workflow automation, and research productivity.
- Advise on prompt engineering and best practices for integrating AI tools into computational research workflows.
- Maintain awareness of evolving policies, standards, and reporting requirements related to AI use in research, including expectations from scholarly journals, funding agencies, and research institutions.
- Promote the responsible and ethical use of AI technologies within the research environment.
Training and Community Support
- Develop and deliver workshops, tutorials, and short courses on scientific computing topics.
- Provide consultation and mentorship to faculty members, postdoctoral researchers, and students.
- Support the adoption of best practices in:
- software development;
- collaborative coding;
- version control systems (e.g., Git/GitHub);
- code documentation;
- reproducible research;
- software maintenance and sustainability.
- Create and curate documentation, guides, and training resources for the TSI community.
- Serve as a liaison between TSI and relevant institutional initiatives, including the McGill Computational and Data Systems Initiative (CDSI).
QUALIFICATIONS
Required
- PhD completed in Physics, Astronomy, Atmospheric Science, Earth and Planetary Sciences, Computer Science, Applied Mathematics, Data Science, or a closely related field.
- Demonstrated expertise in scientific computing and computational research methods.
- Experience using high-performance computing environments and GPU-based computing.
- Experience using AI and large language model tools to support research and software development activities.
- Experience using collaborative software development tools such as Git and GitHub.
- Strong programming skills and experience developing, maintaining, and documenting research software.
- Excellent communication and interpersonal skills, with the ability to explain technical concepts to users with varying levels of computational expertise.
- Demonstrated ability to work independently while supporting a diverse research community.
Preferred
- Experience providing technical consulting, training, or user support in a research environment.
- Experience delivering workshops, tutorials, or educational programming.
- Experience mentoring students, trainees, or researchers in computational methods.
- Familiarity with Digital Research Alliance of Canada resources, CANFAR, or comparable research computing infrastructures.
- Experience promoting reproducible research and software development best practices.
- Research background aligned with one or more TSI disciplines, including astrophysics, cosmology, planetary science, atmospheric science, geophysics, or astrobiology.
- Familiarity with research data management principles and collaborative software development workflows.
What Success Looks Like
A successful Fellow will contribute to a research culture in which:
- researchers and trainees can efficiently access and utilize advanced computational resources;
- researchers and trainees can make efficient and responsible use LLMs to enhance their computational workflows;
- scientific software is well documented, maintained, and shared;
- best practices in coding, version control, and reproducible research are widely adopted;
- AI tools are used responsibly and effectively to enhance research productivity; and
- the TSI community has access to high-quality training and expert guidance on computational research methods.
For inquiries, contact the TSI Program Administrator, Dr. Alexandre David-Uraz at alexandre.david-uraz@mcgill.ca
