The Big Data Analytics for Earth Observation group (https://rsim.berlin) at BIFOLD seeks to employ a postdoctoral researcher in the field of AI Agents for Earth observation (EO) that interact with the users about the environmental and climate issues. Over the past few years, foundation models have fundamentally transformed the landscape of Earth observation, enabling effective and efficient large-scale EO data understanding. Building upon these advances, AI Agents (which are designed to facilitate a seamless interaction with the vast data from the satellite data archives and powered by multimodal foundation models) are rapidly emerging as a central paradigm for decision-making in EO. The selected candidate will conduct research in generative, multimodal, and agentic AI for Earth observation. In detail, the topics include but not limited to design and development of: 1) multi-agent systems; 2) hallucination detection and mitigation strategies; 3) post-training approaches; 4) self-evolving and efficient systems; and 5) evaluation and benchmarking frameworks. Besides conducting research, the successful candidate will have teaching duties, which includes mentoring Bachelor’s, Master’s and PhD students in addition to the coordination of interdisciplinary research projects.
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At the Big Data Analytics for Earth Observation group of BIFOLD, we are seeking to hire a Research Assistant (Doctoral Researcher) interested in the design and development of foundation models for Earth observation. The selected candidate will focus on foundational research and current challenges in this field, including the development of novel algorithms and methods, as well as prototype systems and software tools. Possible topics include multi-modal and cross-sensor representation learning; parameter-efficient finetuning of foundation models; merging of foundation models; and continual learning in the framework of foundation models.
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The Remote Sensing Image Analysis group at the Berlin Institute for the Foundations of Learning and Data is looking for enthusiastic students interested in the development of Large Language Models and Vision-Language Models-based AI agents for Earth observation. In detail, we are looking for applicants who can contribute to the ongoing research work on the development and improvement of AI agents, aiming to make them more reliable, efficient, and collaborative in real applications related to Earth observation (i.e., forest monitoring, crops mapping, etc.).
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