PhD student in Statistics and Machine Learning

We are looking for a PhD student in Statistics and Machine Learning

Your work assignments

We are looking for a PhD candidate to work in the intersection of computational statistics and machine learning, with a particular focus on differential equation-driven frameworks. The research will be fundamentally oriented, and the overall mission is to develop computationally efficient and statistically principled new models and methods, for modern machine learning problems.

Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference.

You will be jointly supervised by Assistant Prof. Zheng Zhao (https://zz.zabemon.com) and Prof. Fredrik Lindsten (https://scholar.google.com/citations?user=GylfPngAAAAJ).

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 applied mathematics, statistics, machine learning, control, 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. The requirement for a degree must be met no later than the time the employment decision is finalized, which occurs when the employment contract is signed. 

The ability to work collaboratively with decent communication skills ia a requirement. The applicant must be able to communicate fluently in both spoken and written English.

It is strongly advantageous if you have excellent study results, a solid background in mathematics and statistics, and strong motivation in theoretical and methodological research.

Experience with statistical machine learning models and methods, Bayesian learning, or an area related to those mentioned in Work Assignments is also strongly advantageous.

Solid programming skills in Python. Experience with JAX/Julia would be a nice plus.

Experience in one or more of the following areas will be considered a strong merit: stochastic (partial) differential equations, controlled differential equations, generative diffusion models, flow models, optimal transport, stochastic filtering, sequential Monte Carlo, Markov chain Monte Carlo, and Bayesian inference and inverse problems is strongly advantageous.

Your workplace

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 (e.g., ICML, ICLR, NeurIPS). 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/organisation/liu/ida/stima.

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 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 10, 2026.

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

In your application, attach: 

• A cover letter (max 1 page) describing yourself briefly. It is important that you explain why you are motivated to apply for this position and how you fit.   

• Curriculum vitae.  

• Transcripts of Bachelor and Master studies in English.  

• A copy of your master thesis and degree certificate. If you have not yet obtained your degree, attach a document briefly describing your thesis work and current status.   

• Contact details of referees (and preferably reference letters if available). 

Applicants with an international background and from underrepresented groups are more than welcome to apply.