The Chair of Data Science in Earth Observation develops innovative signal processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand challenges, such as Global Urbanization, UN’s SDGs and Climate Change, thus, works on solutions that can scale up for global applications. We are involved in a large number of third-party projects and a large international network.
This project is offered as part of a Hans Fischer Senior fellowship through the TUM Institute for Advanced Studies (https://www.ias.tum.de/ias/start/ ) and will be co-supervised by the fellow Prof J. L. Bamber, University of Bristol (https://research-information.bris.ac.uk/en/persons/jonathan-l-bamber), Prof X. Zhu (https://www.asg.ed.tum.de/en/sipeo/home/) and Dr M. Passaro in the Deutsches Geodätisches Forschungsinstitut (https://www.dgfi.tum.de/en/). The aim of the project is to combine a low resolution, thirty year time series of sea surface height (SSH) from satellite altimetry with high resolution data from a new satellite mission (SWOT), tide gauge data and machine learning approaches to reconstruct the 3-D coastal SSH globally. Within the project you will gain skills and knowledge in physical oceanography, climate change, Earth Observation, Big Data and data science as well as machine learning. This is an exciting opportunity to work on an exciting and ambitious project with an exceptional international team with expertise in all aspects of the project.

Your tasks will include:

• Preparation of different EO and in-situ datasets for training a machine learning model
• Development of ML-based spatio-temporal interpolation methods
• Geophysical interpretation and analysis of the results and impact assessment of past and projected future changes along the coast
• Literature research
• Scientific publishing

Your qualifications:

• Completed academic university degree (university diploma / M.Sc.) in Computer Science, Geoscience, Physics, Data Science, or comparable subjects
• Experience in machine learning (ML), artificial intelligence (AI) or related fields
• Software skills in ML languages such as Python
• Ability and enthusiasm to learn new technologies quickly
• Ability to work highly motivated both independently and in a team
• Very good written and spoken English skills
• Some knowledge or background in the geosciences and/or Earth observation is an advantage
• Knowledge of processing spatio-temporal data is an advantage

We offer:

• An exciting and challenging job at a university ranked among the best worldwide
• Compatibility of job and family
• Possibility of remote work (home office)
• A friendly and cooperative environment
• A PhD position remunerated according to TV-L E 13 75% (Tarifvertrag für den öffentlichen Dienst der Länder). The successful applicant will have a 3-year contract. As an equal opportunity and affirmative action employer, TUM explicitly encourages applications from women as well as from all others who would bring additional diversity dimensions to the university’s research and teaching strategies. Preference will be given to disabled candidates with essentially the same qualifications.

Did we catch your interest? We are looking forward to receiving your comprehensive application, including your letter of motivation, CV, and academic transcripts of records, preferably in English via an email to ai4eo@tum.de until 30. November 2025 at the latest. Please indicate “PhD application for Self-supervised Learning of Time Series Data” in the subject line.
Die Stelle ist für die Besetzung mit schwerbehinderten Menschen geeignet. Schwerbehinderte Bewerberinnen und Bewerber werden bei ansonsten im wesentlichen gleicher Eignung, Befähigung und fachlicher Leistung bevorzugt eingestellt.

• Preparation of different EO and in-situ datasets for training a machine learning model
• Development of ML-based spatio-temporal interpolation methods
• Geophysical interpretation and analysis of the results and impact assessment of past and projected future changes along the coast
• Literature research
• Scientific publishin