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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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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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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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, 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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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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Department Community Environment & Policy Department Website Link https://publichealth.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights Manage, conduct, and support firefighter
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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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artificial intelligence (i.e. machine, deep and reinforcement learning…) to optimize efficiency, improve safety, reduce costs and promote sustainability. Collaborate with multidisciplinary teams to uncover a
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applications for faculty positions in Computer Science. Faculty specialising in data science, machine learning (deep learning, reinforcement learning, multimodal learning), Generative AI, and computer graphics
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of almost 11,000 individuals, including approximately 7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. Fueled by curiosity and a deep sense