Vacant job

Your Responsibilities

Physical processes often evolve over both time and space, making spatio-temporal models and data central to large parts of science and technology development, with applications ranging from weather forecasting to cardiovascular medicine. Developing machine learning methods that can model these accurately remains an open and active research area. This PhD project is fundamentally about contributing to this development through new methods for generative modeling, data assimilation, and multi-scale neural network models for spatio-temporal data.

The method development is motivated by a concrete and important application: modeling gas transport and dispersion in urban environments. This is a context that is both scientifically challenging and practically relevant, with connections to air quality monitoring, emergency services' work during hazardous releases, and estimation of greenhouse gas flows for effective emission reductions. Current methods struggle to handle data from heterogeneous sensor networks, are too computationally heavy for real-time use, and provide no reliable quantification of uncertainty.

Addressing these shortcomings is a research challenge both within machine learning and within the application domain. The project tackles both parts in close collaboration with climate researchers.

As a PhD student, you will dedicate yourself to your doctoral education and research projects in which you participate. Your work may also include teaching or participating in other departmental duties, up to 20% of full time. Your responsibilities also include actively contributing to the collaboration within which the project will be carried out (read more under "Your Workplace" below).

NB! When applying for the position, we require you to submit a cover letter (the first field in the application form). This letter should contain a paragraph where you briefly explain/list the qualifications you consider particularly relevant for the research topic described above. This paragraph must start with the words "Suitability for research topic:".

Your Qualifications

You have completed an advanced degree in machine learning, statistics, computer science, fluid mechanics, or a related field deemed relevant for the project's research focus, or completed courses totaling at least 240 higher education credits, of which at least 60 credits are at an advanced level in the aforementioned areas, or have otherwise acquired substantially equivalent knowledge.

Excellent academic results and a strong background in mathematics are required for the position. The applicant must be skilled in implementing new models and algorithms in a suitable software environment, with documented experience. The applicant must also have a strong drive to conduct fundamental research; the ability and interest to collaborate within research; and strong communication skills. The applicant must be able to communicate fluently in English, both orally and in writing.

Your Workplace

Linköping University is one of the leading AI institutions in Sweden.

The position is formally located at the Department of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science (IDA), Linköping University. At STIMA, we conduct research and education in both statistics and machine learning at basic, advanced, and doctoral levels. We regularly publish substantial contributions at the top 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 that clearly integrates the two subject areas within the department. For more information about STIMA, see https://liu.se/en/organisation/liu/ida/stima

The project will be carried out in a collaboration between STIMA (main supervisor: Professor Fredrik Lindsten) and the Centre for Environmental and Climate Science, Lund University (co-supervisor: Professor Natascha Kljun) through a collaborative project within ELLIIT. The project will also employ a PhD student at Lund University, focusing on the applied aspects of the project, while the focus of the advertised position is on the development of machine learning methods. We will strive for close collaboration between the groups, including regular meetings and research visits. As a PhD student in the project, you are expected to actively participate in and contribute to this collaboration.

About the Employment

Upon starting the employment, you will be admitted to the doctoral program. Read more about the respective faculty's doctoral program at https://liu.se/forskning/forskarutbildning

The employment is limited in time to normally four years full-time. Extension of employment up to five years is based on the degree of teaching and departmental duties. In special cases, further extension may occur. You are initially employed for one year, and thereafter the employment is renewed for a maximum of two years at a time, based on the achieved study plan.

Start date according to agreement.

Salary and Benefits

The PhD salary is regulated based on a locally agreed salary scale. Read more about benefits for employees at https://liu.se/jobba-pa-liu/formaner.

Union Contact Persons

Information about union contact persons, see https://liu.se/jobba-pa-liu/hjalp-for-sokande.

Application

You apply for this position by clicking the "Apply" button below. Your application must be received by Linköping University no later than April 24, 2026. Applications received after the deadline will not be considered.