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Field
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at the interface of biostatistics, machine learning, and biomedical data science. This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological
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fully funded PhD position in the area of safe data-driven system identification for cyber-physical systems, offered by our research group at the intersection of control theory, machine learning, and
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multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the interface of computational
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in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications Experience in
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motivated to move the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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the Institute of Psychiatry, Psychology, and Neuroscience (IoPPN) in KCL. The candidate is required to: Have a PhD in Signal Processing, Machine Learning, or Bioengineering from a reputed university. Have a
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Chemical Engineering, Materials Science, or a related field The candidate should have a background in at least one of the following areas: quantum mechanics, molecular simulation, and machine learning
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27 Feb 2026 Job Information Organisation/Company Fondazione Bruno Kessler Department Unità People Innovation Research Field Technology » Computer technology Researcher Profile Recognised Researcher
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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions