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Field
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increasing importance due to the widespread use of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is
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, quantitative characterization, and machine learning-enabled decision-making. The position is associated with the recently awarded ERC Consolidator Grant project “Engineering Multivalency for Superselective
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focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute
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processes of the study systems of our collaborators. Core components of the research involve, among others, Bayesian hierarchical modelling, shrinkage methods, machine learning (ML) or dimension reduction
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CW and pulsed laser systems, spectrometers, high-resolution cameras, and delicate optical components are desirable Expertise in advanced data analysis techniques (Machine learning and Deep learning
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and Distributed Systems Research Group (ND) with co-supervision from IFI’s Machine Learning section and the University of Inland Norway’s research group for User Perception and Engagement in XR
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Intelligence (AI) algorithms, including Machine Learning (ML) and Deep Learning (DL) techniques, for advanced signal analysis. The work will focus on developing methodologies for the detection, extraction
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English PhD Research Fellow in AI for Rehabilitation and Motor Learning Apply for this job See advertisement About the position We invite applications for position as PhD Research Fellow in Adaptive AI
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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resources, flexibility coordination, optimisation, or reinforcement learning. Experience in analytical modelling, optimisation, simulation, machine learning, reinforcement learning, or data-driven methods