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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include
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, such as Seurat and Scanpy. Experience with machine learning models, such as transformer and diffusion models. Strong written and oral communication skills. Modes of Work Positions that are eligible
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RNA metabolism using established human stem cell models of ALS. The post holder will utilise state-of-the-art molecular methods combined with cutting-edge sequencing approaches (through a collaboration
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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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. Proficiency in using PyTorch and other machine learning libraries for developing and implementing machine learning models is essential. Ability to prepare comprehensive written reports, including the use
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with machine learning techniques for robotic decision-making and intelligent control for tasks with high uncertainties. Experience with research on multi-agent collaboration and decentralized control
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Governance Public Health Politics Science, Technology, and Social Movements Patronage and Control of Science and Technology The Politics of Quantification and Modelling Senses and Surveillance Science
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of induced pluripotent stem cell-based human disease models 2) Understanding the role of immunity during tumorigenesis as well as disease relapses in order to design novel inhibitors, CAR-T cell therapy and
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geotechnical engineering, archaeology, geophysics, sensing and machine learning, with group members from seven different countries, speaking over ten different languages. Perks of this role include: Opportunity