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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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About the role – In this post, you will join a collaborative BBSRC-funded project focused on using metabolomics and machine learning to predict lameness outcomes in dairy cows. A typical day may
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: Architect and deploy machine learning and computer vision models directly onto onboard edge devices (e.g., NVIDIA Jetson) for real-time object detection, tracking, and autonomous decision-making. Proof
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. The project integrates synthetic organic chemistry, kinetic analysis, automation, and machine learning to establish next-generation mechanistic workflows for asymmetric organocatalysis. The project advances
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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
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to learn. Good organisational and presentation skills. The ability to work well in an interdisciplinary environment. Further information: This position is offered full time, fixed term for 18 months
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development, machine learning, public interest technologies, digital literacy, sensors, networks, or other EPSRC-relevant technologies. One week you might prototype a low-cost data collection tool; the next you
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. The successful candidate will have a developing professional or academic experience in building and teaching data analytics and machine learning pipelines and play a role in developing modules in areas
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particular emphasis on preparing analysis-ready datasets that support downstream statistical and machine learning workflows. You will work closely with Dr Cynthia Sandor within a collaborative and