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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
., using FEniCSx) Advanced knowledge of scientific programming, preferably in Python, including experience with implementing machine‑learning methods (e.g., PyTorch) Excellent spoken and written English, as
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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systems Experience in deep learning, computer vision, or multimodal data integration Exposure to federated learning, privacy preserving analytics, or distributed systems Knowledge of clinical data models
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contribution of the PhD will be the derivation of multilayered approaches for motion planning and control based on the XS-Graphs, where both model-based and learning-based solutions are foreseen. This includes
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perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results
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environments Interest in industrial monitoring systems, smart sensors, and sustainable manufacturing Experience with sensor data processing or instrumentation systems Knowledge of machine learning or anomaly
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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machine learning (ML) models and optimization algorithms specifically designed for highly dynamic satellite communication (SatCom) systems that can handle networks of varying sizes and configurations
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should hold a Master's degree in Computer Science, Artificial Intelligence, Computational Linguistics, Data Science, or a closely related field Solid background in machine learning and natural
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publications and present them at well-known international conferences and workshops. Your profile M.Sc./M.Eng. Degree in telecommunication engineering, signal processing, machine learning or a closely related