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interdisciplinary team of molecular biologists, bioinformaticians, physicists, and pathologists to develop a biophysically interpretable machine learning model that integrates diverse biological data—including
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schizophrenia. In addition to making fundamental discoveries (e.g. how thalamocortical interactions implement attentional control and task switching), we aim to understand these processes well enough to develop
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or human-computer interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning
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applications for a PhD Research Fellow position in fairness in artificial intelligence, available at the Department of Informatics, in the Scientific Computing and Machine Learning (SCML) research group
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biomaterials, development of wear-testing methods, crown-material processing, mechanical characterisation, surface characterisation, micro-CT and quantitative image analysis. An interdisciplinary and
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the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
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(kidney biopsy, serum, urine) for comprehensive biomarker profiling. Utilization of machine learning and image processing for advanced tissue analysis. The Herman B Wells Center for Pediatric Research
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and data analytics; Development of marine geophysical survey and machine learning algorithms; Process geophysical/geomechanical data and analyze the data using machine learning; Conduct research
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Coordinate and drive research delivery across Immersive Technologies for Heritage Engagement and Preservation projects, with a primary focus on Human Computer Interaction (HCI) evaluation: planning
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techniques. The postholder will contribute specialist expertise in the acquisition, processing and interpretation of EBSD datasets and will help expand the use of these techniques within ongoing research