PhD Student for Sustainable and Resource-Efficient Machine Learning

We are looking for a PhD student for sustainable and resource-efficient machine learning.

Your work assignments

Machine learning has recently advanced through scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality.

Your work will include developing new methodologies and algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as robustness, fairness, and accessibility.

You will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results through publications and research presentations. The exact research direction will be defined jointly with your supervisors.

As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time.

Your qualifications

You have graduated at Master’s level in machine learning, computer science, mathematics, statistics, physics, or a related area that is considered relevant for the research topic of the project, or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in the subject areas mentioned above. Alternatively, you have gained essentially corresponding knowledge in another way. It is required that you are able to communicate fluently in oral and written English.

It is considered advantageous if you have solid programming skills in Python, have good knowledge of LaTeX and version control systems (git), and are comfortable working with (remote) GNU/Linux systems.

It is strongly advantageous if you have excellent study results and a strong background in mathematics. You are skilled at implementing new models and algorithms in a suitable software environment, with documented experience. You have a strong drive towards performing fundamental research, the ability and interest to work collaboratively, and strong communication skills is strongly advantageous.

Your workplace

Linköping University is one of the leading AI institutions in Sweden. We have strong links to prominent national research initiatives, such as WASP and ELLIIT. You will have access to state-of-the-art computing infrastructure for machine learning, e.g., through Berzelius. Linköping University will also host the EuroHPC Arrhenius and a European AI Factory (MIMER), as one of the seven sites across Europe selected in the first batch. Linköping recently won the European Capital of Innovation Awards as the European Rising Innovative city.

The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science. At STIMA, we conduct research and education in both statistics and machine learning, at the undergraduate, advanced, and PhD levels. We regularly publish solid contributions at the best machine learning conferences. STIMA is characterized by a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning which clearly integrates the two subject areas within the division.

The project will be carried out in a 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 Department of Computer Science of the University of Copenhagen in Denmark. We will strive for a tight collaboration between the groups.

The employment

When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University

The employment has a duration of normally four years’ full-time equivalent. Extension of employment up to five years is based on the degree of teaching and institutional assignment. Further extensions may be granted in exceptional circumstances. You will initially be employed for one year, after which your employment will be renewed for a maximum of two years at a time, depending on your progress through the study plan. 

Starting date by agreement.

Salary and employment benefits

The salary of PhD students is determined according to a locally negotiated salary progression.

More information about employment benefits at Linköping University is available here.

Union representatives

Information about union representatives, see Help for applicants.

Application procedure

Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than April 24, 2026.

Applications and documents received after the date above will not be considered.

In your application, please attach:

• A cover letter introducing yourself, your motivation for pursuing a PhD, why you are interested in this project, and how you fit to the position (max. two pages). Please state your preferred starting date.
• Curriculum vitae.
• Transcripts of Master and Bachelor studies.
• A copy (or draft) of your Master thesis. Alternatively, any other type of scientific text from you, e.g., your Bachelor thesis.
• List of publications, if available.
• Contact details of two references and your relation to them.