42 computational-physics Postdoctoral positions at University of Oxford in United Kingdom
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, physics and astronomy, applied mathematics, statistics, computer science, etc.). The Research Associate will need to be proactive, working both independently and as part of ECI/SoGE climate community and
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annum inclusive of Oxford University weighting Potential to under fill at grade 06RS: £34,982-£40,855 per annum inclusive of Oxford University weighting The Department of Computer Science seeks to employ
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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 an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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dynamics and (at intermediate redshifts) strong gravitational lensing, thus preserving and extending the team’s lead in this field. Applicants should have a PhD (or close to completion) in (Astro) physics
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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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We are seeking a full-time Postdoctoral Research Assistant in Physics-Based Modelling of Advanced Batteries to join the Department of Engineering Science under the supervision of Prof Charles Monroe
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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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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explores novel aggregation methods at the intersection of AI safety, computational social choice, and judgment aggregation, aiming to formally integrate multi-stakeholder preferences into AI system design