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Engineering, we are seeking a researcher with a strong interest in developing and applying machine‑learning methods for materials design, in particular steel design. The position is part of our growing research
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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, multi-omics data integration using machine learning, and potential collaborations with clinical and translational researchers. The project is well-suited for candidates with a background in bioinformatics
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Learning has an open position for a doctoral student with a background and strong interest in deep generative learning and computer vision/remote sensing. The successful candidate will join a project funded
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dynamics, initially focusing on quantum-inspired techniques such as tensor networks and tensor-network-based machine learning. You will then investigate how these methods can be reformulated and implemented
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chain, ranging from synthesis, cell assembly, characterization, modeling to scaled-up manufacturing. The 2-year postdoctoral project Machine Learning-based Electro-Chemo-Mechanical Estimation and Control
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methods that combine soundscape targets, acoustic metamaterials, physical modelling, inverse design, machine learning and perceptual evaluation. The postdoc will develop models and design methods
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's like to work and grow at KTH. Trade union representatives Contact information to trade