PhD student in Generative Machine Learning & Computational Statistics

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Your work assignments

We are looking for a PhD student working in the intersection of generative machine learning and computational statistical inference. Generative models based on diffusion processes have emerged as a prominent approach to machine learning with impressive performance in many application domains. A well-known use case is for image generation (these models are the main workhorse for tools such as DALL-E and Stable Diffusion) but the same technology has also shown great promise in applications as diverse as probabilistic weather forecasting, biochemistry, and materials discovery.

Conceptually, training a generative model is similar to solving a conventional statistical learning problem. Guided by this similarity, the research focus of the current position is to answer the questions:

• Can we leverage recent advances in generative AI for solving statistical learning problems?
• Can we leverage state-of-the-art statistical inference methods for improving generative modeling?

We will address these questions through novel methodological research resulting in new machine learning models and computational algorithms. We will also work on applied research to demonstrate the usefulness of the new methods, with particular emphasis on the application domains listed above. This is made possible by our active collaborations with applied researchers and domain experts within all of these fields.

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% of full-time. The work assignments also include actively contributing to the collaborative environment within which the project will be carried out (read more under “Your workplace” below).

N.B. When applying for the position we want you to provide a personal letter (first field in the application form). This letter should contain a paragraph where you briefly explain/list the qualifications that you believe are particularly relevant for the research topic described above. This paragraph should start with the words “Suitability for research topic:”.

Your qualifications

You have graduated at Master’s level in machine learning, statistics, computer science, or a related area that is considered relevant for the research topic of 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 above. Alternatively, you have gained essentially corresponding knowledge in another way.

A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. The applicant should furthermore have a strong drive towards performing fundamental research; the ability and interest to work collaboratively; and strong communication skills. The applicant should be able to communicate freely in oral and written English.

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 and you will have access to state-of-the-art computing infrastructure for machine learning, e.g. through BerzeLiUs. 

The advertised position is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), 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/

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. For more information about STIMA, please see https://liu.se/en/organisation/liu/ida/stima

The project will be carried out in a collaboration between STIMA (main supervisor: Fredrik Lindsten, senior associate professor in machine learning) and the Division of Systems and Control at Uppsala University (co-supervisor: Jens Sjölund, jens.sjolund@it.uu.se , assistant professor in AI). We will strive for a tight collaboration between the groups, including regular meetings and research visits. As a PhD student in the project, you are expected to actively engage in the teamwork and contribute to this collaboration.

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 four years’ full-time equivalent. You will initially be employed for a period of one year. The employment will subsequently be renewed for periods of maximum duration two years, depending on your progress through the study plan. The employment may be extended up to a maximum of five years, based on the amount of teaching and departmental duties you have carried out. Further extensions can be granted in special circumstances.

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 May 3, 2024.

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

We welcome applicants with different backgrounds, experiences and perspectives - diversity enriches our work and helps us grow. Preserving everybody's equal value, rights and opportunities is a natural part of who we are. Read more about our work with: Equal opportunities.




We look forward to receiving your application!





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