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approaches such as flow cytometry or transcriptional profiling. You will be a clear communicator with a track record of high-quality scientific writing and a desire to contribute to a collaborative, ambitious
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quantitative and programming skills along with a track record of designing neuromodulation and neuroimaging studies in healthy participants, of using computer programs to design experimental paradigms, analyse
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motivation, learning and decision making. You will have strong quantitative and programming skills along with a track record of designing neuromodulation and neuroimaging studies in healthy participants, of
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CRISPR, RNAi, DNA manipulation, microscopy, flow cytometry and quantitative real-time PCR. You will have a strong track record of writing papers as evidenced by publications or submitted manuscripts in
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criteria PhD in research methodology, social sciences, or a closely related field (or near completion). Demonstrated experience in participatory, visual embodiment, and interpretive research methods. Track
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completion of, a relevant PhD/DPhil together with relevant experience. You will have the ability to lead and report an original research programme, as shown by a documented research track record, including
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are desirable, as is a proven track record of deploying machine learning models on real-world robot platforms. Informal enquiries may be addressed to Prof. Ingmar Posner (email: ingmar@robots.ox.ac.uk) For more
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) as well as the HEP group in DAMTP. Applicants should have or should soon receive a PhD in a relevant area of physics or astronomy, and have an established track record of original research and
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clear communicator with a track record of high-quality scientific writing and a desire to contribute to a collaborative, ambitious research environment. Essential criteria PhD or equivalent in immunology
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. Track record of writing papers as evidenced by publications or submitted manuscripts in referred journals. Experience in designing and implementing qualitative (and quantitative) approaches to research