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in the deep ocean and help unravel how life emerged on earth. Requirements As a suitable candidate, you have an outstanding Master's degree or comparable degree in physics or a related discipline. You
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generate high-quality experimental datasets, establish process-structure-property relationships, and demonstrate ML-guided optimisation of aerogel electrodes. The outcomes are expected to effectively
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, chemistry, physics, electrical engineering, mechanical engineering, synthetic biology, material science, computer science, micro- and nanotechnology) from a recognised academic institution and the acceptance
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subject – Biomedical Sciences, Biomedical/Information Engineering, Computer Science, Analytical Bioscience, Physics or related disciplines. Prior experience with medical imaging, particularly MRI, medical
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knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video
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computing. The position offers an exciting opportunity to join a cutting-edge research project focusing on the co-design of CMOS and spintronic devices for next-generation energy-efficient and non-von-Neumann
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the largest program in Europe on green carbon conversion, i.e., the Dutch consortium HyCARB. Converting CO2 into valuable chemicals using renewable electricity is a key challenge for the sustainable chemical
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selection process: Your research will align with and contribute to the Discovery Project programme of research through a focus on the contemporary potential for public bathrooms to positively influence cities
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the life sciences and related disciplines (e.g. biomedical sciences, biology, biochemistry, physics, chemistry, psychology, computer sciences, engineering) Acceptance by an authorised academic supervisor
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background in computer science, chemistry, physics, materials science, or a related field. Experience with deep learning frameworks, preferably in the context of generative models. Experience with atomistic