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of the following: Experience with Explainable AI. Experience with Deep Learning. An interdisciplinary background / interdisciplinary training. Have followed courses in Psychology or Philosophy
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equator to pole, from the continental shelf to the deep ocean and from the past to the present. The ocean is Earth’s largest reservoir of CO2 and heat; circulation, mixing, biogeochemistry and other marine
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. Change. Impact! Faculty Mechanical Engineering From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its
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a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks? Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems
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on monitoring complex, superposed deformation signals caused by deep subsurface processes (gas storage, production, and injection) and shallow processes (soil compaction, groundwater dynamics), using satellite
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exploitation of PRIDE (Planetary Radio Interferometry and Doppler Experiment) observations by developing and applying open, reproducible analysis pipelines for deep-space mission tracking. You will be embedded
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well‑developed social skills that enable effective collaboration within a team, and you are driven by curiosity, resilience, and a continuous desire to learn and innovate. Moreover you have: MSc in
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optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter