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machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while enabling more
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dedicated researcher with a PhD in mathematics, electrical or mechanical engineering, computer science or a related field, and a strong methodological background in machine learning. The ideal candidate has
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computer science or electrical engineering from a reputable university On top of systems and/or hardware security, we also consider applicants who have instead worked on computer systems or hardware design topics
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experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and components to automate
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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for Transportation (VITA ) is looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot
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Postdoctoral and PhD Positions in Neurotechnology: Drug/Biologics Carrier Formulation & Chemical Engineering for Focused Ultrasound-Mediated Brain Drug Delivery The Neurotechnology Group of Prof. Mehmet Fatih
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field of research Participate in education, and PhD and master student supervision Profile Strong background in computational biology, bioinformatics, machine learning, or a related quantitative field
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Generation, digital double, human-like avatars. Profile PhD in Computer Vision Background in CS Research experience in gen AI, deep learning Strong computational and analytical skills, as well as experience
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data modalities. AI for scientific discovery: methodological advances with potential applications across biomedical and scientific domains. We are looking for candidates with A PhD in machine learning