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; devising scene completion and occlusion-handling algorithms (e.g., using Zero123, OctMAE) to robustly reconstruct partially visible objects, ensuring accurate simulation for both training and test-time
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and initiatives in the field; participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment; support the Φ
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of privacy-preserving artificial intelligence for the benefit of humanity. What You Will Do: Research (Federated Continual Learning): You will develop novel and privacy-preserving algorithms that allow
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interaction and/or surface flux computation, including familiarity with bulk flux algorithms and observational QA/QC procedures. Experience with processing, analyzing, and interpreting multi sensor
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An already acquired Phd in Electrical Engineering, Computer Science, Applied Mathematics, or a relevant field Affinity for formal and simulation models, as well as algorithmic solutions to problems