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many research synergies coming together on the main thread of machine learning and Artificial Intelligence (AI). The successful candidate will join the newly established research group AI in
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
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Aluminium through Machine Learning, High-Throughput Microanalysis, and Computational Mechanics” - a multidisciplinary research effort at the intersection of machine learning and materials science. This
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areas, engaging in both theoretical and experimental research in: Data-driven and learning based control - Data-driven adaptive motion planning - Cognitive reasoning, symbolic knowledge representation
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the molecular level. While structural predictions using deep learning methods like AlphaFold have revolutionized our understanding of sequence dependent molecular structure, we currently have much more limited
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deployable Machine Learning (ML) solutions integrated into Industrial Internet of Things (IoT) edge devices for condition monitoring. While ML-driven industrial condition monitoring offers significant
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environments.DutiesThe PhD student will carry out research in the area of cooperative autonomous systems. The successful candidate will explore topics such as: Multi-agent reinforcement learning Distributed control
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livable cities. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML
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. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML) methods to tackle