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. In addition to the NIHR funded post available, locally funded posts may also be awarded in parallel, subject to funding and assessment against the stated criteria for the role. Applicants should be
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. Proficiency in statistical methods, programming (including Linux), and use of high-performance computing clusters and parallel computation is expected, along with a clear research vision to enhance the Unit’s
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
developing computational infrastructure, designing machine learning segmentation and classification pipelines, creating visualization and UI tools, processing, storing, and disseminating large datasets, and
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of quantitative methods of data processing and analysis, optimization and scientific programming, in the following areas. * The search, structuration and analysis of economic, energy and emissions-related data
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directly to the upstream swt-initiative31/skija prototype, focusing on: rendering logic. layout behavior. and basic widget implementations. In parallel, they will evaluate how the Skija backend could be
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of geological H2 storage, using isotope ratios (δ2HH2) as a tracer of source or processes. For this purpose, the variability in δ2HH2 due to physical processes needs to be assessed to better constrain
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to the discovery process through algorithm design and development of effective computing techniques What We're Looking For Education and Experience Required: Master’s degree in chemistry, engineering, math, physical
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using different murine and human melanoma cell lines. In parallel, we will also evaluate the photosensitizing agent alone and the molecular construct consisting of the photosensitizer (PPa) conjugated
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. Proficiency in statistical methods, programming (including Linux), and use of high-performance computing clusters and parallel computation is expected, along with a clear research vision to enhance the Unit’s
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Fagan Application Deadline: Applications accepted all year round Details Background: Clostridioides difficile is the leading cause of healthcare-associated infections and is notoriously difficult to treat