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Strong Python skills and experience with data manipulation Basic machine learning knowledge (equivalent to at least an introductory course) Additional skills (nice-to-haves) Experience with website
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spanning artificial intelligence, circuit design, computer architecture, and embedded systems engineering. Where to apply Website https://www.academictransfer.com/en/jobs/362502/phd-on-emulation-framework
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interactions between individual cells. Where to apply Website https://www.academictransfer.com/en/jobs/362788/post-doc-learning-interactions-… Requirements Specific Requirements PhD Degree in Physics Preferably
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the structural and electronic properties of complex materials. Using first-principles simulations, machine learning techniques, and advanced Monte Carlo methods, the student will develop predictive
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23 Jul 2026 Job Information Organisation/Company University of Twente (UT) Research Field Educational sciences » Education Educational sciences » Learning studies Researcher Profile First Stage
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order
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to achieve them. Acquire new knowledge quickly and use existing knowledge in new ways. Work constructively under pressure or in the face of adversity. Demonstrate strong problem-solving abilities with a
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Documented ability to work in Python Experience with machine-learning methods for record linkage and text analysis Documented experience with machine-learning methods for image-to-text transcription
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applicability. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse