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Field
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needs, we may support flexible and hybrid work arrangements. Options will be discussed during the interview process? Responsibilities The division runs several research projects in parallel. The candidate
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utilise the University of Birmingham’s smart campus building data in order to develop a template for such an assessment. In parallel, working the Met Office and their forecasting team, the project will
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? parallel computing, resource allocation, multiple threads, etc. Proficiency in configuring jobs for a parallel environment, specifically allocating adequate memory and nodes to meet specific time/budget. In
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others. Ability to coordinate with faculty, graduate and undergraduate students, and external collaborators. Preferred Qualifications: Strong background in parallel computing, parallel application design
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project partners. In parallel, support will be provided for the implementation and operationalization of the financial, economic, structural and risk performance indicators developed within the NEXUS
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develop and compare ex vivo model systems derived directly from patient tumor tissue and blood. These models will be evaluated in parallel with an upcoming clinical trial at our department testing
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be controlled, measured, and related to the thermal and mechanical properties of the foams. Several formulations and systems will be studied in parallel to shed light on these three issues
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together the parallel efforts of 4 climate modeling centers, including IPSL. The successful candidate will be responsible for integrating, evaluating, and testing new parameterizations of air–sea carbon
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Centre Euopéen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS) | Toulouse, Midi Pyrenees | France | 20 days ago
). It hosts several scientific teams covering several topics (climatology, fluid mechanics, parallel algorithms...) among which the CFD (Computational Fluid Dynamics) team is the largest. The main fields
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training