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Field
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research experience in e-health, digital health or a related field experience of, or a documented interest in, machine learning, AI methods or large language models (LLMs) in clinical or health-related
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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/machine learning/AI related disciplines German language skills A1 required/recommended, no proof required English language skills C1 required, please provide an official language certificate, e.g
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or qualification, field of scholarship, and accomplishments in the field. Minimum Number of References Required 2 Maximum Number of References Allowed 3 Keywords Machine Learning Reinforcement Learning Foundational
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engineered mouse models, cell culture, flow cytometry, immunostaining, microscopy, molecular biology and bioassays; Collects and safeguards project data; keeps detailed, accurate and up-to-date notes detailing
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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Humanities Distributed Systems High-Performance Storage Machine Learning Medical Informatics Neural Data Science Practical Informatics Scientific Information Analytics Software Engineering for Distributed
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and batch processing. These efforts provide the foundation for advanced analytics, machine learning, and AI applications. The IDE Research School guides PhD researchers by offering a platform for
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are interested in combining disciplinary knowledge with the skills of a data scientist and working at the interface of bioinformatics, medical informatics, databases, data mining, machine learning, applied
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Strong Python programming skills and familiarity with machine-learning frameworks (e.g., PyTorch), data engineering, SQL and version control Interest in longitudinal clinical data, clinical terminologies