PhD student in Spatio-Temporal Machine Learning

Your work assignments

We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods.

Our world is inherently spatio-temporal, i.e. physical processes around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from weather forecasting to cardiovascular medicine. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as enablers for predicting and reasoning about dynamics and transport patterns, in turn being of key importance for a broad range of human activities.

The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi-scale neural network models.

While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas dynamics in urban environments. Gas dynamics shape air quality, greenhouse gas fluxes, and emergency response capacity. Yet, current modelling approaches are incapable of inference from diverse sensory data, too computationally demanding for real-time use, and lack reliable uncertainty quantification. The methods developed in the project will tackle these shortcomings, enabling computationally efficient inference and prediction of gas dynamics at high spatial and temporal resolution, and in turn more effective climate change mitigation, urban air quality management, and rapid response to hazardous releases.

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. 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, fluid mechanics, 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. You will have access to state-of-the-art computing infrastructure for machine learning, e.g. through Berzelius. 

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, read here.

The project will be carried out in a collaboration between STIMA (main supervisor: Prof Fredrik Lindsten) and the Centre for Environmental and Climate Science, Lund University (co-supervisor: Prof Natascha Kljun) through an ELLIIT collaborative project. The project will also employ a PhD student at Lund University, focusing on the applied aspects of the project, whereas the focus for the advertised position is on the machine learning method development. 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 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 February 6, 2026.

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