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of physical systems AI-based condition monitoring Reinforcement learning Programming skills are required, with Python experience preferred. Theoretical understanding and hands-on experience with electric
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, or similar platforms for thermal and fluid dynamics simulations. Strong programming skills in MATLAB, Python, R, or similar. Machine learning or data-driven modeling experience applied to environmental control
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a postdoc in the field of machine learning and decisions applied in cooling systems as per
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nuclear physics detectors. Experience analyzing data from high energy or nuclear physics experiments. Familiarity with Monte Carlo simulations. Familiarity with machine learning techniques. About the
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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competencies Education You should have completed within the past five years or be close to completing a PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
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mathematics or a related field. The successful candidate will have expertise in at least in one of: Machine learning in the context of physical systems AI-based condition monitoring Reinforcement learning
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, whether actual or perceived by others (including service-connected disabilities), gender (including pregnancy related conditions), military status or military obligations, sexual orientation, gender
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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the