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employment. Publication Track Record: At least two first-author research papers at flagship venues of core machine learning research (e.g., NeurIPS, ICML, ICLR, AISTATS). Theoretical Rigor: A deep
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-established field of research, these are still very much open problems. To this end, we are looking for candidates interested in developing new machine learning methods for medical image analysis, with a
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be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with the ambition of producing contributions relevant to leading venues in
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topics: Development of deep learning models Mathematical foundations of machine learning Optimal deployment of AI systems The candidate is expected to hold a relevant MSc degree in Computer Science
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efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will be expected to conduct original, high-quality research
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intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types of materials are fed into
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cognitive support and corrective feedback. You will apply advanced algorithms for machine learning, multimodal biosignal processing, and human-state inference, working with shared-control strategies and
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machine learning methods to explore how meltwater — at the surface and at the ice sheet bed — is coupled to ice flow. Building on this, you will also examine how meltwater and ice dynamics influence
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, machine-learning methods and predictive calculations of defects and transport properties. The position will also be integrated into the wider CENSEMAT research environment at Aarhus University, allowing
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, particularly ADHD, using large-scale genetic, imaging, and epidemiological data from Denmark and other countries. You will apply advanced analytical approaches, including deep learning and machine learning, to