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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
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, human-computer interaction, psychology, and sociology. We conduct both fundamental and applied research, striving to contribute to today's major issues. For example, we study how we can improve
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of closed-loop brain–computer interfaces aimed at restoring motor function in models of motor disorder. Some part of the role may involve teaching and up to 0.1 FTE of your time may entail teaching activities
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-informed machine learning) and integrating uncertainty quantification into these workflows. You are familiar with environmental or soil science applications (e.g., carbon, nitrogen, biomass modelling). You
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multimodal deep learning models that integrate imaging and clinical data to personalize treatment and follow-up strategies. In the Netherlands, around 75% of patients with an abdominal aortic aneurysm (AAA
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-based knowledge with machine learning. You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop and implement surrogate and hybrid modelling
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simulations. Job Description Are you passionate about bridging computational modeling with clinical cardiology to solve real-world healthcare challenges? We're seeking a PhD candidate to develop innovative
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-computer interaction, or related field. Interest and experience with AI/ML concepts and demonstrated ability to learn technical tools quickly. Experience or interest in urban planning and climate adaptation
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learning, and systems engineering. Where to apply Website https://www.academictransfer.com/en/jobs/354917/phd-postdoc-on-model-predictive… Requirements Specific Requirements We are looking for talented