PhD Position in Spatio-Temporal Machine Learning (Statistics, Computer Science, Fluid Mechanics) Linköping University in Sweden

Linköping University in Sweden is offering a fully funded PhD position in Spatio-Temporal Machine Learning, with a focus on developing novel methods for analyzing and predicting spatio-temporal processes. The research will be conducted at the Division of Statistics and Machine Learning (STIMA), Department of Computer and Information Science, in collaboration with the Centre for Environmental and Climate Science at Lund University. The project is part of a collaborative initiative (ELLIIT) and will involve close cooperation between the two universities, including regular meetings and research visits.

The PhD project centers on the development of 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 methods will be broadly applicable, there is a particular emphasis on inferring gas dynamics in urban environments, which is crucial for air quality management, climate change mitigation, and emergency response. The research aims to address current limitations in modeling approaches, such as computational inefficiency and lack of reliable uncertainty quantification, by enabling efficient and accurate inference and prediction at high spatial and temporal resolutions.

Applicants should have a strong background in machine learning, statistics, computer science, fluid mechanics, or a related field, with a Master's degree or equivalent qualifications (at least 240 credits, including 60 advanced). Excellent academic results, strong mathematical skills, and documented experience in implementing models and algorithms are required. Candidates should be highly motivated for fundamental research, possess strong collaborative and communication skills, and be proficient in English.

The position is fully funded, with salary and benefits according to Linköping University policy. The employment is typically for four years full-time, with possible extension up to five years depending on teaching and departmental duties. The successful candidate will be expected to contribute to the collaborative research environment and may have teaching or other departmental responsibilities up to 20% of full-time.

Supervision will be provided by Prof. Fredrik Lindsten (Linköping University) and Prof. Natascha Kljun (Lund University). The application deadline is February 6, 2026. For more information and to apply, visit the official vacancy page.

Key research areas: spatio-temporal machine learning, statistics, computer science, fluid mechanics, gas dynamics, AI, graph neural networks, data assimilation, urban air quality, and climate science.