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background. Demonstrated experience designing and building scientific instruments, including the integration of optical, electronic, mechanical, and computer-controlled subsystems. Hands-on expertise in one
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, drawing on machine learning where it strengthens these methods. The research supports mission-critical scenarios and feeds into an end-to-end resilience proof of concept developed together with Swedish and
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. Qualifications Applicants at Postdoctoral Researcher level should hold a PhD in AI enabled learning, educational technology, information systems, computer supported learning, social entrepreneurship, innovation
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, drawing on machine learning where it strengthens these methods. The research supports mission-critical scenarios and feeds into an end-to-end resilience proof of concept developed together with Swedish and
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at the interface of automatic control, electrochemistry, and machine learning. The position will also involve close collaboration with another postdoctoral researcher working on a complementary project in physics
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive
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. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. Marine
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including high-impact scientific publishing, and collaborative research with international teams. Who we are looking for Requirements MSc degree in Computer Science, Data Science, Machine Learning, or a
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. Interest and experience with training/fine-tuning machine learning models would also be appreciated. Interest and knowledge of economic theoretical modelling would be a plus. Strong coding skills and
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. Essential qualifications PhD in computational statistics, bioinformatics, systems biology, or a related field. Strong expertise in statistical methods, including machine learning approaches, for analysis