103 genetic-algorithm-computer "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at University of Oxford
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. The post-holder will be responsible for managing their own academic research programme in Salmonella effector biology. You will have a high degree of autonomy to develop the methodology and experimental
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shelves, the breakup of which can speed up flow of grounded ice and affect global sea level, and on the highly specialised Antarctic biodiversity. This ambitious programme brings together leading UK (BAS
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We are looking to appoint a postdoctoral researcher, to work with a group of UK Higher Education Institutions to deliver a programme of mental health research. The work is funded by the Medical
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programme grant with partners across the UK to facilitate the use of hydrogen for aviation, and in particular the icing vulnerability of heat exchangers and parts of the airframe. You will work to generate
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engineering, computer science or other field relevant to the proposed area of research. You should have a good track record of robotic publications/presentations in the field of healthcare, possess sufficient
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and Prof Paul Shearing. The post is funded through a strategic research partnership and is fixed term for up to 2 years. To support the programme, the post holder will be required to carry out research
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Modernising Medical Microbiology (MMM) unit at the University of Oxford (https://www.expmedndm.ox.ac.uk/mmm). You will be joining a highly interdisciplinary team of approximately 40 clinicians, computational
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the performance of lithium ion technologies. To support the programme, the post holder will be required to carry out research on characterisation of battery degradation, with a particular focus on the application
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methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly