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scientific curiosity. You thrive at the boundary of robot learning, computer vision, deep learning, and simulation, and you are excited to see your research running on real robots. You communicate clearly
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: machine learning or deep learning (e.g. PyTorch) scientific data pipelines or large datasets knowledge graphs or structured data systems GPU or distributed computing scientific machine learning or physics
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flows, or reinforcement learning-based design optimization. Strong programming skills in Python with experience in PyTorch, JAX, or equivalent deep learning frameworks. Ability to work independently
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Expertise: Deep experience in Large Eddy Simulation (LES) and a solid understanding of Atmospheric Boundary Layer (ABL) physics. Programming: Strong proficiency in C++, Fortran, or Python, and experience
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from the areas of few-shot learning, continual learning and modular deep learning, as well as different LLM alignment frameworks, based on reinforcement learning and direct preference optimisation
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languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; The interest and ability to share knowledge with other ESA organisational units. You should also
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the concepts we use, they generate deep uncertainty and concern about their impacts, and they transform the social and conceptual contexts within which ethical evaluation takes place. It is an open question
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to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental
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develop a deep understanding of, and subsequently address, a complex societal challenge, namely the accessibility of urban navigation. The postdoctoral researcher will join a multidisciplinary research team
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(HIMS), in close collaboration with industrial partner BOR-LYTE and Smart Industry testbeds. This position offers a unique opportunity to combine inorganic chemistry, spectroscopy, machine learning, and