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Experience with using inference/machine learning tools and basic programming is a plus As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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, modelling, and AI to turn scientific research into real-world impact. You’ll join an open, supportive environment that fosters learning and professional growth. Job requirements Must haves: Master’s degree in
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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to participate in the creation of complex machines that have never existed before? Do you enjoy sharing your knowledge with younger group members? If yes, then maybe you want to work as a research assistant. The
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Are you eager to support exciting research with ultracold atoms and trapped ions? Do you want to participate in the creation of complex machines that have never existed before? Do you enjoy sharing
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, tools, or artistic creations, humans instinctively explore the unknown in order to acquire information about it, to make sense of it, to act on it, and to appreciate what is in front of them
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, Industrial Engineering, or related discipline; Affinity and/or experience with computer programming, statistical learning, and optimization techniques; A good team spirit and feel at home at the intersection
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degree in Computer Science, Artificial Intelligence, Data Science, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong
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-ACCENT project aims to fill this gap by combining insights from cognitive psychology, neuroscience, AI engineering, human-computer interaction and social science, with lifespan perspectives. Using advanced