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Field
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curious to deliver work that matters, your journey starts here! As one of the oldest forms of applied science and engineering, Carnegie Mellon University’s Department of Metallurgical Engineering was
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development of simplified continuum-based models, enabling fast assessments across large quay wall networks. This position is part of the Bridge and Quay Walls (Bruggen en Kademuren) programme of
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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apply. Your application will receive fair consideration. Challenge. Change. Impact! Faculty of Electrical Engineering, Mathematics and Computer Science The Faculty of Electrical Engineering, Mathematics
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2). Vascular network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering The Disease Biophysics Group is a creative, transdisciplinary group of engineers
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. Through academic, clinical, and industry partnerships, as well as global networks, we strive to translate biological discoveries into applications that enable the early detection of deviations from health
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an area related to COSMOS, including degrees in operations research, industrial engineering, statistics, computer science, and mathematics. Preferred Qualifications Completed dissertation research related
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of Contract Extra Information MSCA Fellowship Job Status Full-time Hours Per Week 40 Offer Starting Date 8 Apr 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Reference
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in electrical engineering, computer engineering, computer science, or similar. Strong background in communication systems, optimization, or machine learning for networked systems. Experience and
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Science. The main research themes are Artificial Intelligence, Computational Science, and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative