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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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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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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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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
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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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properties of the Higgs boson. The group focuses on final states containing several tau-leptons. The analysis activity is now extended to include generic anomaly searches using Machine Learning. Furthermore
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anomaly searches using Machine Learning. Furthermore, the group takes part in ATLAS upgrade, with participation in the ITk-Pixels project, with responsibilities concerning testing and delivery of pixel
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Intelligence (AI) and machine learning, digital sociology, AI and the sociology of knowledge, AI and expertise, and digitalization and criminology (digital criminology). A prerequisite for employment is that