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., high performance, functional array programming DSLs) to tackle challenging probabilistic and differentiable programming applications (e.g., experimental design, machine learning for science). We do so by
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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creation that controls clogging patterns Developing predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated
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mathematical modelling, with a focus on real-world applications. This includes statistics, uncertainty quantification, data analysis, signal processing, (mathematical foundations of) machine learning, and
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-antibodies. You will focus on the identification of these antibodies by using mass spectrometry based de novo sequencing, machine learning and AI-tools to interpret the data. Your job The primary objective
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. Contribute to university education on your topics of investigation and related ones. Where to apply Website https://www.academictransfer.com/en/jobs/355119/phd-potential-of-ai-in-planning… Requirements