1,291 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof"-"Prof" positions at Nature Careers
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computers, and especially fault-tolerant quantum computers, for high-value decarbonisation use cases such as improved battery chemistries, the development of new catalysts for hydrogen, biofuel or ammonia
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of Excellence for Data-Driven Discovery, applying advanced computational techniques to develop novel therapeutics. This position will work closely with researchers in the Center of Excellence for Data Driven
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As a fellow you will join our faculty in the Department of Biostatistics, providing statistical support and developing innovative biostatistical methods for research projects at the cutting edge
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mathematics. We are looking for a visionary personality with a forward-looking research agenda who will promote interdisciplinary collaboration and is willing to participate in the ongoing development in
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artificial intelligence (AI) initiatives by developing and training AI tools aimed at automating and optimizing clinical workflows, operational efficiencies, and administrative tasks. As part of our dynamic
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for Humanity. What you would be doing: Research – As a part of the Dyson School of Design Engineering, you will actively develop and lead your own research programme, in line with our research themes and vision
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drug experts (clinicians, clinician scientists, data scientists, and laboratory investigators) to co-develop phenotyping algorithms but is expected to serve as the domain expert in high-throughput
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, including image acquisition, processing, analysis, and interpretation Develop and validate new imaging techniques, algorithms, or software to improve diagnostic accuracy and patient outcomes Collaborate with
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and interpretation. Prominent examples include time sequences on groups and manifolds, time sequences of graphs, and graph signals. The objectives The project aims to develop unsupervised online CPD
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the development of more efficient online learning algorithms for manifold-valued data streams, with an initial focus on change-point detection, opening the door to new unsupervised data exploration methods. Next