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Field
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evaluation strategies. In close collaboration with chemists, engineers and data scientists, a platform is being developed that combines materials development, process optimisation and machine learning
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please
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analysis. • Experience with, or a strong interest in, machine learning, probabilistic modelling, statistics, signal processing, digital health or wearable sensor technologies. • Programming
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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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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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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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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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, 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