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Field
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described. From a technological perspective, optimizing radiative cavities is critical to improving the performance of TPV subsystems. Efficient cavity designs must maximize useful photon flux reaching
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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doctoral schools in the natural sciences and one in the humanities and social sciences as well as numerous smaller research training groups. Advising The consulting team of the Graduate Academy’s Service
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strong background in Mathematical Optimization and/or Numerical Analysis is desirable. Completed the previous degree with an excellent GPA (top 10% of class as a guideline) Proficiency in English to be
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theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization, or Materials Science. More specifically, we are looking for candidates with
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, or geometric deep learning. Experience with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage
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with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage. Experience and abilities
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. MINDnet aims at addressing the challenge through a holistic optimization - from individual computing devices to the overall architecture, including a focus on applications, and training methods - across
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-efficient, open-loop optimisation of fermentation control profiles, building on recent theoretical developments in optimal control theory, reinforcement learning and numerical methods as well as laboratory
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Description The PhD researcher will work on the Inventory-Routing Problem with Pickups and Deliveries (IRP-PD) under demand and supply uncertainty. The IRP-PD requires the joint optimization of inventory and