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disciplines, including human-robot interaction, robot learning, soft robotics, computer vision, and agricultural robotics. About the PhD project: We are looking for a highly motivated and talented PhD research
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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Postdoctoral Research Fellow in Ethics and AI Apply for this job See advertisement About the position Integreat – Norwegian Centre for Knowledge-driven Machine Learning at University of Oslo is looking for a
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active role in developing novel machine learning based systems and tools on the path towards clinical use and implementation of AI for the treatment and care of individuals also from minority populations
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. In addition, the nature of the interaction between human and machine triggers new questions about the locus of agency and learning these emergent collaboration ecologies. Such examinations may require
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dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All candidates and projects will have to undergo a
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on how collaborative practices evolve with these powerful tools, and support learning in disciplinary or interdisciplinary contexts. In addition, the nature of the interaction between human and machine
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data collection approaches. Familiarity with or strong motivation to learn machine learning or advanced data analytics for pattern detection and forecasting in environmental data. Familiarity with
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning