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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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analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will work in close cooperation with staff and our current PhD students. PhD research fellows receive
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models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside developing own research ideas, applicants should be capable of turning those ideas
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models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants primarily hinges on their documented academic qualifications and
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repeated for a database of events covering different sea ice types, conditions, locations, and rates of ice deformation (from docile to violent). Machine learning techniques will then be used to find a
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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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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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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility
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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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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility