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Sebastian Mair

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PhD Student Position in Sustainable and Resource-Efficient Machine Learning Linköping University in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Apr 24, 2026

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Country

Sweden

University

Linköping University

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Keywords

Computer Science
Environmental Science
Mathematics
Python Programming
Accessibility
Carbon Emissions
Statistics
Machine learning

About this position

Linköping University, one of Sweden's leading AI institutions, invites applications for a PhD student position in sustainable and resource-efficient machine learning. The project focuses on developing methodologies and algorithms that reduce computational, energy, memory, and storage demands in machine learning, aiming to minimize associated carbon emissions while maintaining model quality. Research topics include data selection and filtering, model compression and simplification, hardware-aware optimization, and the interaction of resource efficiency with broader sustainability aspects such as robustness, fairness, and accessibility.

The position is based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science, with collaboration between STIMA (main supervisor: Assistant Professor Sebastian Mair) and the Sustainable Artificial Intelligence for Sciences (SAINTS) Lab (co-supervisor: Assistant Professor Raghavendra Selvan) at the University of Copenhagen. The research environment offers access to state-of-the-art computing infrastructure, including Berzelius, EuroHPC Arrhenius, and the European AI Factory (MIMER). Linköping University is recognized for its innovative research and strong links to national initiatives such as WASP and ELLIIT.

As a PhD student, you will design and run reproducible experiments, measure resource metrics, implement prototypes in Python, and communicate results through publications and presentations. The exact research direction will be defined jointly with your supervisors. Teaching or other departmental duties may comprise up to 20% of full-time.

Applicants must hold a Master’s degree in machine learning, computer science, mathematics, statistics, physics, or a related area, or have completed at least 240 credits (with 60 in advanced courses) in relevant subjects. Fluency in English is required. Advantageous qualifications include strong Python programming skills, knowledge of LaTeX and git, comfort with GNU/Linux systems, excellent academic results, a strong mathematics background, and documented experience in implementing models and algorithms. Collaborative ability and strong communication skills are highly valued.

The position offers full-time employment for four years, with possible extension up to five years based on teaching and institutional duties. Salary is determined according to a locally negotiated progression, and employment benefits are available. The starting date is by agreement.

To apply, submit a cover letter, CV, transcripts, a copy or draft of your Master thesis (or other scientific text), list of publications if available, and contact details of two references. Applications must be received by April 24, 2026. Linköping University values diversity and equal opportunities. For more information and to apply, visit the provided links.

Funding details

Available

What's required

Applicants must have graduated at Master’s level in machine learning, computer science, mathematics, statistics, physics, or a related area relevant to the project, or have completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in the subject areas mentioned. Alternatively, essentially corresponding knowledge gained in another way is acceptable. Fluency in oral and written English is required. Solid programming skills in Python, knowledge of LaTeX and version control systems (git), and comfort with (remote) GNU/Linux systems are advantageous. Excellent study results and a strong background in mathematics are strongly advantageous. Documented experience in implementing new models and algorithms in a suitable software environment is required. Strong drive for fundamental research, collaborative ability, and strong communication skills are also advantageous.

How to apply

Apply online by clicking the 'Apply' button on the position page. Submit a cover letter, CV, transcripts of Master and Bachelor studies, a copy or draft of your Master thesis (or other scientific text), list of publications if available, and contact details of two references. Applications must be received by April 24, 2026.

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