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field. Strong research background in recommendation systems, graph neural networks, and multimodal representation learning. Solid programming and model development skills in machine learning and deep
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will design and validate advanced multi-agent Deep Reinforcement Learning (DRL) and/or Digital Twin (DT)-enabled methods for efficient, scalable and time-critical handover optimisation. The work will
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multisensor fusion and ondevice AI pipelines that guarantee tight latency, power efficiency, and fail-safe robustness. Driving hardware–software codesign to radically optimize state estimation and deep-learning
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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project at the forefront of microscopy and bioimage analysis (https://www.msca-agile.eu/ ) and contribute to OMERO integration and optimization of Biom3d, a cutting-edge deep-learning framework for 3D image
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testing (NDT), and the mechanics of advanced materials, particularly composites and fatigue-critical structures. A key research direction involves the integration of machine learning and deep learning
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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effectiveness depending on image depth, and availability of multi-sectral information will be analysed. Related mathematical skills Basic knowledge of AI / deep learning methods is needed. Having some interest in
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We seek a Ph.D., M.D./Ph.D., or M.D. scientist with deep expertise in data science, advanced machine learning/AI, or bioinformatics methods and applications in multi-omics and multimodal data (eg