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, and clinical data Develop and apply computational, statistical, and machine learning methods for biomarker discovery and risk prediction Investigate molecular mechanisms linking immune aging
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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interaction, geo-information science, or a related field, with experience in experimental research using virtual reality? If so, we have an exciting PhD opportunity for you! Spatial thinking plays a crucial
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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, biomedical engineering, advanced image processing and machine learning. The studentship suits a candidate with a strong background in optometry, physics, engineering, computer science or a related discipline
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international conferences on complex dynamical systems and related areas of mathematics. Besides your main research activities, you will teach one course per semester for most semesters while you are employed
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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modeling of protein dynamics We are seeking a highly motivated PhD student to join a DDLS-funded project at the interface of structural proteomics, protein biophysics, and machine learning. The position is
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from machine learning methods to more traditional statistical and econometric techniques. We are driven by science with purpose, pushing the academic frontier by publishing at the highest level in
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps