525 parallel-processing-"NTNU---Norwegian-University-of-Science-and-Technology" Fellowship positions in United Kingdom
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months, in the first instance. Application Process If you would like to discuss this role please contact Prof Regent Lee at regent.lee@nds.ox.ac.uk Applications for this vacancy are to be made online. You
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to contribute to an exciting European project developing hydrothermal processes for the management of solid digestate and other waste from the olive oil production process. About the Role We are seeking a
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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to work with modern massively-parallel simulation codes. Candidates must have (or be close to completion of) a PhD in astrophysics or a related subject, and a BSc/MPhys (or equivalent) degree in physics
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include high-performance computing, middleware and computing systems. Recent relevant research experience to include: Undertaking research in the area of high-performance / distributed / parallel computing
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, and improve linkages between atmospheric chemistry and public health research. The position is part-time and will require close interaction with parallel research activities at the University
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frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience in scalable data processing, including the use of parallel computing, cloud platforms, and distributed systems for efficient