356 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions at University of Oxford
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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About the role As a system administrator, you will play a critical role in supporting, maintaining and developing the computer systems upon which all the Department’s IT operations depend. Your work
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open-source software, including autodE, cgbind/C3, and mlp-train, and are pioneering new frameworks for training Machine Learning Interatomic Potentials (MLIPs). We are seeking a highly motivated
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Internal candidates will be given priority for this recruitment and must apply via the Employee Dashboard. About the Role The Oxford Applied and Theoretical Machine Learning group at the Department
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at the intersection of artificial intelligence, multi-omics integration, and cellular systems modelling. Based at the Big Data Institute (BDI) at the University of Oxford, the successful candidate will join the Ideker
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, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
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: Earth Sciences, Bioscience, Interdisciplinary Life and Environmental Science, Inorganic Materials for Advanced Manufacturing, Chemical Synthesis for a Healthy Planet,Statistics and Statistical Machine
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interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning, cutting-edge radio instrumentation and digital signal processing, citizen
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, enthusiastic and willing to learn new skills. You will have highly effective verbal and written communication skills with all level of staff and an ability to operate effectively in a demanding and
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application of AI and machine learning models to interpret complex X-ray datasets, and the integration of experimental and computational insights to generate actionable knowledge that advances sustainable metal