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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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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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proficient in conducting quantitative analyses. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside
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conducting quantitative analyses. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants
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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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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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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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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
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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