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approaches, CECS offers Bachelor of Science degree programs in four majors: Electrical Engineering, Mechanical Engineering, Computer Science, and Data Science. In addition, CECS starts a PhD program in
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behavioural, physiological, and environmental acoustic variables. This post would suit someone with a PhD in biomedical engineering, data science, machine learning, experimental psychology, audiology, cognitive
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contributing to course and programme administration and supervising Graduate Teaching Assistants. Candidates must have a completed PhD in Psychology or a highly related cognate area; have strong evidence of
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carry out a related attractive scientific research program. We expect the professor to attract new space missions to the University of Vienna and represent Austria in the respective international mission
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About Us The Faculty of Life Sciences & Medicine (FoLSM) at King’s College London is one of the largest and most successful centres for biomedical research and education in the UK. The Faculty has
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patient cohorts, geometric deep learning, computational anatomy, cardiovascular modelling, multimodal datasets. What you’ll need Applicants should have a PhD (or nearing completion) in computational imaging
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& Qualifications needed A first degree in Computer Science, Mathematics, Physics, Electronic/Electrical Engineering, or a related field A PhD (or equivalent experience) in a relevant discipline Solid knowledge
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computational partners who will provide candidate antigen predictions from tumour RNA and genomic datasets. It is essential that you currently hold a PhD/DPhil (or close to completion) in a relevant field such as
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level or equivalent, with substantial numerical content, eg mathematics, statistics, computer science, or physical science. You will have demonstrable programming ability using a statistical package (e.g
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assessments (EMA), and passively monitored behavioural, physiological, and environmental acoustic variables. This post would suit someone with a PhD in biomedical engineering, data science, machine learning