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fellowships. About You The ideal candidate will hold (or near completion of) a PhD/DPhil in computational cognitive neuroscience/psychology, computer science, or relevant quantitative field, and a demonstrably
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Computer Science, Optical Communication Engineering, or related field. Candidates at the PDRA level must have a PhD Degree in Computer Science, Optical Communication Engineering, or related topics. Applicants should
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Contract: 2 years (24 months – full-time) We are looking for an excellent post-doctoral candidate with a PhD / DPhil (or near completion) in quantum optics, solid state quantum physics, magnetic
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being used by the clinical group in parallel with neurosurgical patients in Iowa. Our goal is to advance medical science by providing insights on the neural mechanisms underlying auditory cognition and
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
in the Amazon and on how these relate to the distribution of vector-borne diseases in the Amazon forest. The post holder will carry out their research, advised by the PI and Dr. Milton Barbosa from
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scholars in Law and two PhD students (one in Law and one in Computer Science/Data Analytics), as well as with international, European and national stakeholders involved in the CURE project. The post-holder
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C and python, familiarity with
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the risks. You will have: a PhD in one of the relevant STEM disciplines, such as mathematics, statistics, computer sciences, theoretical food, ecological or physical sciences, etc. skills in mathematical
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models