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dynamics, data science, and machine learning are beneficial. Please submit your detailed application with the usual documents by August 15, 2025 (stamped arrival date of the university central mail
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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(master’s or equivalent) with excellent grades in the field of data science, material science, mechanical engineering, physics, or similar, with a strong machine learning or simulation background In-depth
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Mathematics/ Approximation Theory to be filled by the earliest possible starting date. The Chair of Applied Mathematics, headed by Prof. Marcel Oliver, is part of the Mathematical Institute for Machine Learning
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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(master’s or equivalent) with excellent grades in the field of data science, material science, mechanical engineering, physics, or similar, with a strong machine learning or simulation background In-depth
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written and spoken English skills High degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very
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: Master’s degree in a scientific or engineering subject with excellent grades; enthusiasm for neuroscience and brain mapping; openness to new methods such as deep learning. Offer: Funded PhD position (TV-L 13
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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multidisciplinary educational background, and possess a Master's degree in physics, physical chemistry, chemical engineering, materials science, nanoscience, mechanical engineering, or a closely related discipline.