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to circadian rhythms in population health. This post is part of a large, interdisciplinary research programme, offering attractive opportunities to work across conventional boundaries and pursue hypothesis-led
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research initiative funded by ARIA, titled Aggregating Safety Preferences for AI Systems: A Social Choice Approach. The project operates at the interface of AI safety and computational social choice, and
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in wide-bandgap solids are an example, where the deterministic interaction between emitted photons and electronic and nuclear spins enables photon mediated entanglement for distributed quantum networks
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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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Contract: 36 months, full time An exciting opportunity has arisen for a Postdoctoral Researcher to join the Howard Group, working on cutting-edge science in the field of Computational and Systems
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the Department of Engineering Science at the University of Oxford. The post is funded by the Oxford Martin Programme on Circular Battery Economies. It is fixed term up to December 2027. You will undertake
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM orchestration, with applications to Finance. This post is advertised as full-time (35 hours per week). We...
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. Computing time is available on our local cluster and on the Vienna Scientific Cluster (VSC), a supercomputer shared by Austria's major universities. We focus on the development of methods to solve the many