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Field
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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has been studying any of these topics: statistical physics, computer simulation methods, and polymer physics. Proficiency in the C++ and/or Python programming language is an advantage. Good knowledge
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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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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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quantitative modeling; Strong expertise in programming, including proficiency in languages commonly used in data analysis and machine learning, such as Python; Excellent verbal and written communication skills
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development in coordination with the interests of its scientists. As such, there are plenty of opportunities to learn new skills, expand your knowledge, collaborate across disciplines, and experiment in a
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approaches that integrate both qualitative and quantitative research (e.g., combining big data or machine learning with in-depth fieldwork) can also be pursued. Method selection and mastery are viewed as part
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this position, the chosen candidate will conduct research on how machine learning techniques or XAI can be leveraged by heuristic algorithms, or conversely, how heuristics can be enhanced by incorporating machine
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models