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DTU Management invites applications for a 3-year PhD Scholarship associated with the project “Brains in Motion: Explaining How Humans Learn and Adapt to Navigate Urban Environments Using Artificial
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Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr | M lheim an der Ruhr, Nordrhein Westfalen | Germany | about 3 hours ago
Institute für Kohlenforschung. Group members will therefore have the chance to learn highly diverse techniques, depending on their research focus and interest. Applications Please send your application
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on components of your University Teaching Qualification (BKO) portfolio. Your supervision team will consist of Dr Sanne Weber (main substantive PhD supervisor) and Dr. Haley Swedlund. Would you like to learn more
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learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and tissue reconstruction. The PhD candidate will work with
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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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bioinformatics, statistical genetics, cheminformatics, database development, and the application of machine learning approaches. Project description The PhD project will investigate how genetic variants alter GPCR
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bookkeeping (general ledger) experience. Familiarity with Cornell sponsored awards. Ability to learn Cornell University's applications (Webfin2, Kuali, Research Administration Support System, Workday, and
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, you will: Acquire and analyse human neuroimaging data, with a primary focus on high-field fMRI of natural sound perception. Develop and apply AI/NeuroAI models, including deep neural networks, to model
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knowledge graph from scientific papers and cognitive test questionnaire data, and second, to integrate the graph with transformer-based large language models and causal learning. This offers an explainable
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired