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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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computational methods with 9-month academic appointments to begin in Fall 2027. Associate Professor and Professor candidates will be considered for tenure review upon hire. We seek applicants whose research
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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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University Lisbon, for the development of teaching and research activities in the disciplinary field of Humanities, namely in the fields of Digital Humanities and Quantitative Methods. The teaching and
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Biotechnology with focus on Translational Quantitative Biomedical Imaging: an early-career researcher who will build an independent program developing and applying quantitative biomedical imaging methods and/or
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-noise effects and domain shift, and develop methods that improve trustworthy and equitable model performance. Contribute to clinically informed acoustic modelling, working closely with the Southampton
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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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Institution Name: Łukasiewicz Research Network – Krakow Institute of Technology Position Title: Research Specialist (Implementation of PhD) CZM_24/2026 Currently, as part of the research and scientific
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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