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learning, representation learning, and fair, accountable, and transparent machine learning. This PhD position in machine learning, will lead to a joint doctoral degree from KTH Royal Institute of
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and 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
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bioinformatics-driven analyses, multi-omics data integration using machine learning, and potential collaborations with clinical and translational researchers. The project is well-suited for candidates with a
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and interpreting results, and benchmarking brain-inspired architectures against conventional machine learning methods. You are also expected to actively participate in academic discussions with
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work on a step change in the methodologies by using Machine Learning, whose benefits will cut across these funding streams. The student’s work will focus on using ML to generate workflows that enable
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data to develop predictive lifetime models using statistics, signal processing, and machine learning. Validating degradation models on laboratory motor test benches under inverter excitation. You will
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experience in AI/machine learning. This project is funded by Vinnova under the FFI (Strategic Vehicle Research and Innovation) programme and is carried out in collaboration with Autoliv. Supervision
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discovered. The successful candidate is expected to investigate the use of advanced machine learning techniques to enhance both the analysis and mitigation of fault attacks. The insights gained will be
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fluid 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
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professional approach, analyze and work with complex issues and demonstrate experience with programming languages such as Python or C++. We seek candidates with a background in machine learning, robotics and