72 postdoc-distributed-algorithms positions at University of Oxford in United Kingdom
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The Oxford Agile Initiative has awarded funding for an exciting 12-month research project exploring the macroeconomic and distributional impacts of the UK’s Seventh Carbon Budget. Developed in close
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calendars, the preparation and distribution of agendas and taking, transcription and distribution of minutes, and follow up of action points arising from meetings. You will be educated to at least A-Level
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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incorporated into cellular function. We are now looking for a postdoc to study the physicochemical properties of membraneless organelles and the links to neurodegenerative disease. The candidate will have
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team. You will lead in the design and implementation of statistical and computational algorithms of different datasets, and implement novel algorithms within the framework of existing code, providing
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developing mathematical algorithms and simulations in MATLAB, in particular with Semidefinite Programming and Sum of Squares and of the analysis and design of feedback control systems using these approaches
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mentoring junior researchers and collaborate with faculty, DPhil students, and postdocs across engineering, computer science, government, and law disciplines. The role is full time 2 years fixed term
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2025. Interviews will be held as soon as possible thereafter. At the Dunn School we are committed to supporting the professional and career development of our postdocs and research staff. To help them
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. Armed with this information, the post holder will use cutting-edge paleoclimatic modelling that incorporates nutrient cycling and carbon chemistry (HadOCC) to infer the distribution of potential feeding
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the