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the team, to effectively collaborate, and to communicate in a diverse scientific environment High proficiency in spoken and written English Interest in learning effective usage of emerging computational
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Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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be done in close collaboration with the other members of the FADOS network. Further information on the project is available at: www.fz-juelich.de/en/ibi/ibi-3/organization/neuroelectronic-interfaces
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, government entities, industry partners, NGOs and citizens – to collaboratively make sustainable change. Through transdisciplinary action research, the consortium investigates conditions for collective learning
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approaches that combine artificial intelligence, machine learning, natural language processing, and social sciences. This collaborative and cross-sectoral approach aims to produce robust methods for evaluating
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research we expect you to: Conduct cutting-edge research in neuro-behavioral sciences using advanced analytical techniques. Collaborate with clinical researchers and collect data from a large cohort using
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addresses the following central research question: how can we design human-AI collaboration to mitigate biases and foster equitable decision-making? To answer this question, the research will explore areas
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groundbreaking research, providing high-quality education, offering excellent support, and fostering collaborations within and outside the university. In doing so, we contribute indispensably to a healthy, free
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; BSS) in collaboration with the top clinical hospital Ziekenhuisgroep Twente aims to develop such a health condition monitoring system to assist patients’ recovery management and ultimately reduce the