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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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neurophysiological mechanisms shape political attitudes and behaviour, as well as how politics shapes and exploits our affect and emotions. This Fellowship is addressed to early career researchers with a PhD in
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. The post-holder will have the opportunity to teach. Applicants should hold PhD in Astrophysics or a related field. A strong background in Radio interferometry observation and sufficient specialist knowledge
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doctoral students. Results should be presented at national and international meetings as well as published in leading subject and high-impact publications. Applicants should hold a PhD/DPhil, (or close
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, regenerative biology, or olfaction Interest in microbiome-host interactions Experience of molecular biology PhD in a relevant area Experience of imaging Team worker Desirable criteria Original research
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to their own research interests. About You Candidates should have a PhD in a relevant discipline or will have obtained it by commencement of the position. Candidates should have some experience in multi
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. The post-holder will have the opportunity to teach. Applicants should hold PhD in Astrophysics or a related field. A strong background in Radio interferometry observation and sufficient specialist knowledge
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hold a PhD/DPhil or be near completion of a PhD/DPhil in a relevant biological discipline. Good communication and documentation skills, and the ability to work effectively as part of a team are essential
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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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posters locally and at national and international conferences, as well as participate in MRC grant’s research programme led by Prof Marco Fritzsche. You must hold a relevant PhD/DPhil (or be near completion