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This is a broad call for five fully-funded PhD positions in computer science and engineering to work on machine learning, autonomous systems, software engineering, formal methods, and network
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technologies for medical diagnostics, treatment, and monitoring. Our research activities span computational modeling, algorithm development (using both traditional signal processing and machine learning
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on applying AI and machine learning to molecular design challenges. This position is one of several industrial PhD roles funded by the DDLS program, which supports training in four strategic areas: cell and
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Wiberg is “Innovative statistical and machine learning methods for comparing performance and outcome in register data studies”, with overall aim to develop, evaluate, and implement innovative statistical
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of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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academia and industry. Requirements The following qualifications are required: Solid knowledge in mathematics and statistics, in areas such as linear algebra, probability theory, machine learning, high
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML
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of geometric deep learning and add rigorous arguments to a debate driven by empirical results. Who we are looking for We seek candidates with the following qualifications: To qualify as a PhD student, you must
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collaborative). Personal research synopsis for the PhD project (max 3 pages A4, font Arial 11 pt, single line spacing). It should be written and authored by yourself (not machine-generated e.g. using AI text