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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning
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knowledge of syntax-based statistical analysis tools. Strong skills in developing reproducible and transparent analysis workflows. Solid background in machine learning and analysis of large and complex
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(or equivalent) in Computer Science, Machine Learning, Mathematics, or a related technical field. For Postdoctoral Fellows: A completed PhD in one of the fields mentioned above and a strong publication record
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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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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 patients. The candidate will contribute broadly
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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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physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidates will work in close cooperation with our current PhD students within the PhD programme, and
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expertise in the following areas: Machine Learning in general, with an emphasis on deep learning and language modeling Model benchmarking and evaluation pipelines for NLP/LLMs Domain-aware application of AI
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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