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This postdoctoral position focuses on securing large-scale distributed AI systems against network-based threats. The research will explore network attack surfaces in distributed AI, assess
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to contribute your own research ideas and take part in supervising PhD students. About the research project The position, starting in the first half of 2026, will be based in the theory division of the Department
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The position, starting in the first half of 2026, will be based in the theory division of the Department of Microtechnology and Nanoscience (MC2), which hosts research ranging from quantum transport and quantum
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project focuses on developing and refining advanced organ-on-chip technologies to study and optimize nanoparticle-based delivery systems in biologically relevant environments. The project is strongly
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us The position is based in the Division of Materials and Manufacture at the Department of Industrial and Materials Science (IMS), Chalmers University of Technology. Our division conducts cutting
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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abundant side streams into high-value bio-based materials, contributing directly to the circular bioeconomy. You’ll work with cutting-edge bioprocessing tools, collaborate with leading European partners, and
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on LLM-based risk analysis and demonstrate its applications in various risk control measures. You will collaborate closely with other researchers at the division and contribute to building a shared
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) for turbulent hydrogen mixing and combustion. Optical and laser-based diagnostics will be used on well-defined reference cases to deepen the understanding of hydrogen’s mixing and reactivity. Numerical work will