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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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foundational neural models, where models learn from large unlabelled image datasets, but also on additional data like clinical reports or electronic health rec-ords. The work will be done in collaboration with
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge
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analysis. • Hydrological and hydraulic simulation. • Machine learning, including unsupervised clustering and predictive modelling. • Working with large, complex, multi-source datasets using MATLAB, Python
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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for Horticulture and Phenotyping) team research topics focus on low cost computer vision and machine learning, simulation assisted plant phenotyping and machine learning based data mining for plant biology
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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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of written and spoken English. You should have experience with programming (e.g. Python, Julia), simulation methods (e.g. molecular dynamics) and modern machine learning methods. Additional Information