PhD on Self-supervised Learning of Time Series Data

14.11.2024, Wissenschaftliches Personal

The chair for Data Science in Earth Observation at TUM is looking for a PhD (m/w/d) (TV-L E13 75%) starting on 1. January 2025. The position is limited to 3 years initially.

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, e.g. 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.

The expected tasks of this position will consist of scientific research on the topic of artificial intelligence for Earth observation as well as teaching and project tasks. The main research goal is to develop innovative self-supervised learning methods for time series multi-modality Earth observation data, taken into account the different spatial temporal scale of the Earth observation data. The capability of the model shall also be demonstrated in various downstream tasks.

Your tasks will include:

• Preparation large EO datasets for the training of machine learning model
• Development of innovative self-supervised learning methods for time series EO data
• Evaluate the machine learning model on downstream tasks
• Literature research
• Project reporting
• Teaching

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, e.g. 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.

The expected tasks of this position will consist of scientific research on the topic of artificial intelligence for Earth observation as well as teaching and project tasks. The main research goal is to develop innovative self-supervised learning methods for time series multi-modality Earth observation data, taken into account the different spatial temporal scale of the Earth observation data. The capability of the model shall also be demonstrated in various downstream tasks.

Your tasks will include:

• Preparation large EO datasets for the training of machine learning model
• Development of innovative self-supervised learning methods for time series EO data
• Evaluate the machine learning model on downstream tasks
• Literature research
• Project reporting
• Teaching

• Completed academic university degree (university diploma / M.Sc.) in Computer Science, Geo-Informatics, 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
• Good written and spoken English is an advantage
• Knowledge of signal processing algorithms for time series data such as videos is an advantage

• An interesting and challenging job at university ranked among the best worldwide
• Compatibility of job and family
• Posibility 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 2024 at the latest. Please indicate “PhD application for Self-supervised Learning of Time Series Data” in the subject line.

Kontakt: ai4eo@tum.de