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, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role
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, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role
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, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role
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meetings Engage in analysis and writing of findings, as author or co-author when substantively involved Share results with stakeholder audiences Write and publish on existing datasets Qualifications: PhD in
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 10 days ago
simulation tool for modelling hydrogen isotope transport in materials. The role is central to enhancing the scientific reliability and benchmarking capacity of FESTIM in support of fusion fuel cycle modelling
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take responsibility for the planning, execution and analysis of high-quality research, ensuring the validity and reliability of data at all times and will maintain ongoing scientific discussion with
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on reliability, security, and resilience of electric power systems and microgrids and stability analysis and Scientific Machine Learning (SciML) for microgrid applications. The successful candidate will be
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difficult and the creation of more intelligent process control strategies and innovative methods of tracking reliability can be achieved with expert informed machine learning techniques, which offer more
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cooperative perception setting. Semantic-Aware Compression & Network Information Theory: Derive new rate–distortion–reliability bounds, design adaptive codecs that prioritise safety-critical bits, and close
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difficult and the creation of more intelligent process control strategies and innovative methods of tracking reliability can be achieved with expert informed machine learning techniques, which offer more