PhD student in Spatio-Temporal Machine Learning
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Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning methods that can accurately model such processes remains an open and active research frontier. This PhD project is fundamentally about advancing that frontier, contributing new methods for generative modelling, data assimilation, and multi-scale neural network architectures applied to spatio-temporal data.
The development of these methods is motivated by a concrete and important application: inferring gas transport and dispersion in urban environments. This setting is both scientifically demanding and practically far-reaching, with applications related to air quality monitoring, emergency response to hazardous releases, and greenhouse gas flux estimation. Current approaches struggle to assimilate data from heterogeneous sensor networks, are too computationally demanding for real-time deployment, and lack reliable uncertainty quantification.
Addressing these shortcomings is a research challenge in both core machine learning methodology and the application domain. This project will tackle both in close collaboration with climate scientists (read more under “Your workplace” below).
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.
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. Linköping University will also host the new 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. 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: 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 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 April 24, 2026.
Applications and documents received after the date above will not be considered.
We look forward to receiving your application!
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