62 assistant-professor-computer-science-and-data-"Prof" Fellowship positions at University of Oslo in Norway
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for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and neuroscience. The candidate is expected to assist
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must hold a degree equivalent to a Norwegian doctoral degree in epidemiology, biostatistics, computational biology, or a related field, with a strong quantitative background. Doctoral dissertation must
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written communication skills in English. Desired qualifications: Expertise in broader topics in computer science, information theory, statistics, or mathematics. Publications in top conferences/journals
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with respect to academic credentials. Qualification requirements: Master’s degree or equivalent in Artificial Intelligence Computer Science Foreign completed degree (M.Sc.-level) corresponding to a
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advanced bioinformatic analyses of genomic data. The candidate will join a collaborative and interdisciplinary team with expertise in Arctic biology, genomics, and evolutionary theory, and will have the
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degree or currently working on finalizing master thesis in computer science, statistics, mathematics, data science, or related fields. Strong background in statistics and linear algebra. Foreign completed
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motivation regarding completing the research training program Documented experience with research within the educational field, preferably learning sciences or educational psychology. Personal skills In
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combining the mathematical and computational cultures, and the methodologies of statistics, logic and machine learning in unique ways, Integreat's machine learning will solve fundamental problems in science
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more information, please see our web site . Qualification requirements Applicants must satisfy the requirements for admission to the faculty´s PhD programme. This normally includes: Applicants must hold
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of Informatics, Uni-versity of Oslo, and will be part of a growing research agenda at the intersection of epidemiology, statistical modeling, machine learning and public health data systems. The project aligns