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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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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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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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of potentially novel modes of protein binding is possible in collaboration with other members of the lab. Desired (but not absolutely required) skills: programming in python, machine learning, and experience in
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architecture exploration, hardware/software co-design and operating/runtime systems. Typical application domains are e.g. signal-/image processing, artificial intelligence and machine learning. Tasks: research
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applications for a PhD Student or Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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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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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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direction (possibly forming spontaneous “lanes”), crossing, and opposite flows. For single-lane vehicular traffic, the model should revert to a car-following model. In cooperation with the supervisor Dr