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to computational methods for imaging. You have: a Master's degree in mathematics, applied mathematics, computational science, mathematical physics, or a closely related field; a strong background in one or more of
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Digital manufacturing, Industry 4.0, or cyber-physical systems o Product design for disassembly, remanufacturing, or recycling o Data analysis, AI, or machine learning applied to engineering systems
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PhD position in detector development for the next generation of cryogenic dark-matter axion exper...
The Astroparticle and Dark Matter group at the Center for Cosmology and Particle Physics Phenomenology (CP3) at the Department of Physics, Chemistry and Pharmacy at the University of Southern
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-materials in which materials research and physical testing form the validated foundation, enabling Digital Twins and AI-based decision support for design and process choices. This full-time position offers
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in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video
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Understanding factors related to student retention and experience in physics and astrophysics major units. Using quantitative (surveys) and qualitative data (interviews with students) this project
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physically within the State of Maryland. PRIMARY DUTIES • Perform routine laboratory maintenance and research activities such as: general housekeeping, daily observations of animal health, collection
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of Power-to-X processes for renewable fuel production. The project is primarily rooted in process engineering while also addressing the interaction between Power-to-X plants and the electrical grid. You will
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of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear
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process. This project offers a unique opportunity to work at the intersection of machine learning and control theory. You will develop rigorous theory and scalable computational methods for certifying