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include the development of finite elements methods, as well as inverse design strategies based on deep-learning and Neural Networks approaches. The latter will then bring the project to the experimental
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Praha 120 00, Czechia [map ] Subject Areas: Statistics, data analysis, information theory, machine learning, deep learning, and data science Appl Deadline: 2026/04/16 04:59 AM UnitedKingdomTime (posted
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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date specified in AP Recruit to learn whether the department is currently reviewing applications for a specific position. If there is no future review date specified, your application may not be
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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of this PhD is to develop physics-informed neural operator frameworks that embed governing equations and invariants of fluid mechanics directly into learning architectures, enabling real-time, generalizable
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of Programmable and Intelligent Networks Position You will work actively on the preparation and defence of a PhD thesis Edge Intelligence for 6G Networks. The PhD project will deep dive into Edge Intelligence
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within SCI and across other departments within Pitt, and initiatives like the $11.6M Western Pennsylvania Quantum Information Core (https://www.pitt.edu/pittwire/features-articles/pitt-investment-pa
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(optimal) solutions—with subsymbolic approaches such as deep learning and reinforcement learning to reduce the complexity of knowledge acquisition and search for solutions. Therefore, this project is closely