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We are seeking a full-time Postdoctoral Research Associate in Machine Learning for Grid-Edge Flexibility to join the Power Systems Architecture Lab within the Department of Engineering Science
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PhD in Chemistry or a relevant subject area, (or be close to completion) prior to taking up the appointment. The research requires experience in computational chemistry, including machine learning
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(DPAG), Oxford University. You will work alongside leading experts in mitochondrial metabolism, and engaging with postdoctoral scientists, PhD candidates, and research staff. In this environment, you will
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projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
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system planning, system modelling, multi-criteria decision analysis, data science, and machine learning. You will perform stakeholder engagement and on-the-ground data collection in LMIC contexts and
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Scientist to join the research group of Professor Stephanie Cragg on a discovery project in the field of striatal neurobiology. The ideal candidate will hold a PhD in a relevant area of neurobiology and
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, experience in bioinformatics, such as developing computational pipelines or applying advanced machine learning algorithms, and supervising student research would be desirable. Applications for this vacancy
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colleagues in partner institutions, and research groups Selection criteria: Essential: The post holder must hold or be near completion of a DPhil/PhD in the life science. Hands-on experience with confocal
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• Assist in IP protection and commercialisation activities Selection criteria: • PhD or equivalent experience in a relevant field • Experience with grant writing, IP management, and research
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criteria: • Hold, or be near to completion of, a PhD/DPhil in chemistry, materials science, nanotechnology, or related field. • Experience in surface chemistry design for biosensing and disease