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Master Thesis - Enhancing Regression Performance in Geo-Foundation Models for Earth Observation Applications with Multi-Temporal Learning

Forschungszentrum Jülich GmbH -
Jülich

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Vollständige Stellenbeschreibung

Master Thesis - Enhancing Regression Performance in Geo-Foundation Models for Earth Observation Applications with Multi-Temporal Learning

Earth observation is key to tackling global challenges such as sustainable agriculture, climate change, and environmental monitoring. With your contribution, you will help developing the next generation of AI foundation models, enabling more accurate and reliable Earth observation applications with real-world research and societal impact. Join us now at the Jülich Supercomputing Centre (JSC).

Vertragsart: Vollzeit

Angebot

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:

Impactful Research: Work on a cutting-edge, societally relevant research topic within an ESA-funded international project
Practical relevance: With us, you will gain valuable practical experience alongside your studies and actively participate in interdisciplinary projects
Excellent Scientific Environment: Collaborate with leading researchers from international research institutes, universities, public organizations, and industry partners.
Access to World-Class Computing Resources: Conduct experiments on modern HPC infrastructure, including JUPITER, one of Europe’s most powerful supercomputers.
Work-life balance: We offer flexible working hours to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on-site) appointments
Fair remuneration: We will pay you a reasonable remuneration for your thesis In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits

Aufgaben

In this thesis you will work in the European Space Agency (ESA)-funded project Fast-EO (Fostering Advances in Foundation Models via Unsupervised and Self-Supervised Learning for Downstream Tasks in Earth Observation). You will investigate novel approaches to improve the capabilities of Geo-Foundation Models (GeoFMs) for Earth Observation (EO) applications, with a particular focus on downstream regression tasks such as biomass estimation and crop yield prediction. GeoFMs are typically pretrained on large-scale EO datasets using self-supervised learning, enabling them to learn general-purpose representations that transfer across a wide range of applications. However, despite their strong transfer capabilities, GeoFMs sometimes fail to match the performance of task-specific supervised models on downstream regression tasks. A key challenge lies in effectively modeling the temporal nature of EO data. Since downstream applications require varying temporal resolutions and sequence lengths, from daily observations to monthly or yearly time series, current temporal modeling strategies are often suboptimal and struggle to generalize across diverse tasks. In addition, the limitations of existing pretraining objectives and architectures for regression problems are still not well defined. In this thesis, you will investigate the factors limiting the performance of current GeoFMs and develop novel methods to improve their temporal representation learning and downstream regression performance. The goal is to design more flexible temporal modeling strategies and improved pretraining approaches that enable GeoFMs to generalize more effectively across a broad range of real-world EO applications.

Profil

Currently pursuing a master’s degree in Computer Science, Data Science, Artificial Intelligence, Geosciences, Physics, or a related field
Strong programming skills in Python and experience with deep learning frameworks such as PyTorch, TensorFlow, or scikit-learn
Experience with multimodal and/or multitemporal data. Experience with Earth observation or regression datasets is an advantage
Experience working on High-Performance Computing (HPC) systems, including batch scheduling and Slurm
The ability to work effectively in an interdisciplinary team
Well organized, reliable, proactive, and motivated to conduct independent research
Very good command of written and spoken English with extensive vocabulary is required (at least B2 level according to the CEFR), ideally supported by a certificate confirming the language level

Über uns

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us. The following links provide further information on diversity and equal opportunities: https://go.fzj.de/equality and on specific support options for women: https://go.fzj.de/womens-job-journey

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