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discipline. Experience with deep learning framework PyTorch or similar. Strong background in machine learning, image or signal processing. Knowledge of SotA models for multi-modality and scene understanding
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skilled in object-oriented coding (preferably Python) and data analysis; affinity with machine learning and explainable AI techniques, preferably in a geoscience context; good social skills. As a university
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contribute to developing this theoretical framework, with a strong focus on analytical modeling, computational methods, and the interpretation of learning signals embedded in physical structures. Recent
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with modern machine learning. You will work on extending data-driven models with process-informed constraints and novel data integration strategies. The position is embedded in the Computational
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evaluation under regulatory constraints (e.g., AI Act considerations). You also possess: experience with machine learning and model development; affinity with explainable AI, recommender systems, or hybrid AI
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11 Apr 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Computer engineering Engineering » Systems engineering Researcher Profile
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Models to push the frontier where computer vision, physics simulation, and embodied AI converge. Join Us! This position is part of a collaborative research programme between the University of Amsterdam
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causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via consultations and collaborative research, train researchers through workshops, and mentor
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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researchers to design and develop a wide range of innovative projects, for example involving causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via