182 scholarship-phd-agent-based-modelling Postdoctoral positions at University of Oxford
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choice theory, or computational modelling. This post is based at the Department of Computer Science and on-site working is required. Remote and part-time working is possible in agreement with Professor
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, reporting to Prof. Mauro Pasta. Applicants must hold PhD/DPhil in Materials Science or Chemistry (or be close to completion), together with relevant experience in the field of beyond Li-ion batteries, in
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the project will focus on developing a thermal water splitting process based on complex transition metal oxides, and then studying the kinetics of the process to facilitate the design of a reactor to integrate
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form, how it is generated and how it evolves. In particular we focus on the evolution and evolvability of vertebral counts, and we use various species of Lake Malawi cichlids as our model organism
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human tumour models including organ/tumour perfusion, slice culture and organoids to ensure data is clinically relevant and to inspire the next generation of effective treatments. The post would suit
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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base, the partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will
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developing new optogenetic treatments for inherited retinal diseases. We are looking for highly qualified individual (PhD/DPhil) with outstanding academic credentials, who can work well under pressure and
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in preventing immune-mediated pathology in autoimmunity remains poorly understood. Using genetic and antibody-based targeting, we aim to dissect how these pathways modulate T-cell signalling
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(LiB’s). You will be responsible for: • Developing models and simulations of the electrode fabrication process, sensors, and actuators. • Developing a demonstrator of a soft sensing system that