We are now looking for a PhD student in machine learning with a focus on generative modeling and data-centric strategies for data-efficient machine learning with considerations for fairness and privacy aspects.

The position is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP). You will take part in the WASP curriculum and benefit from its extensive network of other PhD students and senior researchers.

Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden's largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems.

The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society and industry.

Read more: https://wasp-sweden.org/

The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry.

Read more: https://wasp-sweden.org/graduate-school/

Your work assignments

Machine learning, and in particular deep learning, requires large amounts of data and energy resources for training. At the same time, there are significant challenges in addressing dataset bias and privacy concerns, especially in applications that deal with sensitive data, such as medical diagnosis. The focus of this PhD project is to develop methods for reducing the dataset size without significantly affecting the performance of a model trained on that data, to promote efficient optimization and reduce the computational demands, while at the same time addressing fairness and privacy aspects. The goal is to promote resource-efficient and trustworthy machine learning in a joint framework.

In your work, you will explore generative modeling for creating synthetic representative datapoints with a high training value, while considering dataset bias and making sure that sensitive information is not leaked from the real dataset. You will work with different types of datasets (from low-dimensional point sets to high-dimensional image data) and targeting different types of applications (e.g., medical imaging). The work will be both theoretically oriented, as well as focused on implementation of experiments with machine learning algorithms for empirical testing.

As a doctoral 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% of full-time.

Your qualifications

You have graduated at Master’s level in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related field, or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses (in the 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. Moreover, it is considered advantageous if you have a strong interest in machine learning efficiency, fairness, and (differential) privacy.

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. Furthermore, strong communication skills are highly valued.

The project involves both theoretical and applied work.

Great emphasis will be placed on personal qualities and suitability.

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 project will be conducted as a collaboration between the Division for Media and Information Technology (MIT) at the Department of Science and Technology at Campus Norrköping (principal supervisor Gabriel Eilertsen) and the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science at Campus Valla in Linköping (co-supervisor Sebastian Mair).

The MIT division researches and conducts education in areas such as computer graphics, image analysis, scientific visualization, and machine learning, at undergraduate, advanced, and postgraduate levels. Our research group at the division consists of more than 20 PhD students, senior researchers, and research engineers. We conduct research projects in areas spanning from deep learning and medical imaging to computer graphics and computational photography.

The STIMA division conducts 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 student will be affiliated with both divisions and have a desk at each campus (the two campuses are approximately 42 km apart). We strive for close collaboration between the two groups, giving you access to the expertise and networks of both

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.

This position may come to be a security classified position. If so, security screening including a records check will be carried out before any decision on employment is made.

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 August 21, 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 the position (max. two pages). Please state your preferred starting date.
• Curriculum vitae.
• List of publications, if available.
• 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.
• Contact details of two references and your relation to them.