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validated. In parallel, CEA-List has developed µArchiFI [3-4], a formal framework enabling pre-silicon analysis of fault injection effects at RTL level, from hardware implementation details up to software
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to their work. About You We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate: Essential Criteria: PhD (or near completion) in engineering, maths
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within the School of Natural Sciences. You will provide inductions, training, supervision and assistance in techniques, use of equipment and relevant software to researchers and students. You will be
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will provide inductions, training, supervision and assistance in techniques, use of equipment and relevant software to researchers and students. You will be experienced at working both independently and
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, and formal methods for the robustness, performance, correctness, and security of code generated by AI or interacting with AI. Areas of interest include the coordination and validation of concurrent AI
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mixed-methods analysis plans to effectively blend qualitative and quantitative data using established mixed-method approaches · Design and implement multiple method analysis plans when formal
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and experience in software development or formal methods – ideally in functional programming, formal specifications,or theorem proving. Essential Application/interview Familiarity with a proof assistant
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(one) Research Scholarship (BI) to a student enrolled in a PhD programme, to carry out R&D activities within the scope of the project “FUTURURAL – Futures of Rural Urbanism: South-North Dialogues based
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, analytical chemistry or similar a PhD degree is considered a plus a solid understanding of protein chemistry, analytical chemistry, MS-omics techniques and bioinformatics a strong background in mass
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
and build novel data resources and statistical methods to enhance and improve clinical data science, and (3) create curricula and training resources for a clinical data science education program at UNC