57 phd-architecture-and-urbanism Fellowship positions at UNIVERSITY OF SOUTHAMPTON in Uk
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a highly motivated and enthusiastic Early Career Bioinformatician to apply and develop their computational skills in a clinical research setting. This is a unique opportunity for a recent PhD graduate
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university, while collaborating with national and international partners across the Missed Vital Sign programme. You will have a PhD (or equivalent professional qualification and experience) in biomedical
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learning, the topology and geometry of data, or the dynamics of learning. The successful candidate should have, or be expecting soon to receive, a PhD in Mathematics, or related field, with demonstrated
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Fellows are expected to lead their own projects, collaborate on others, and guide PhD and Master’s students. Essential qualifications, experience and competencies PhD (or near completion) in human T cell
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is required to have a PhD in operations research, mathematics, computer science or a related area. Experience in discrete optimization, demonstrated by at least one publication, is essential. Strong
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profession. We have excellent experimental facilities including a suite of acoustic chambers. We welcome applicants from diverse backgrounds who meet the following criteria: A PhD (or equivalent) in acoustics
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arm with active thermography approach selected), and support demonstrations - trials within the industrial environments selected within the project. About you You will hold a PhD or equivalent and will
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invited to contact Dr Benjamin Cerfontaine (b.cerfontaine@soton.ac.uk ) for further information about this position in advance of submitting your application. Applicants are required to have a PhD* or
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usually have been gained through a relevant PhD, but may also have been achieved via other research experience, normally supported by a relevant postgraduate degree (e.g. MSc in Health Psychology, digital
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods