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Field
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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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data science and machine learning Additional software skills such as Shiny, LaTeX, Tidyverse, Tableau, C/C++, Java, GitHub Experience in report writing for projects underpinned statistics Attributes and
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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
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Boreal forest recovery after clear-cut: We are looking for a highly motivated PhD student to collect data on and model hydro(geo)logy and greenhouse gas exchanges of boreal forests after the disturbance. Boreal forests constitute the second largest forest biome after the tropical rainforest,...
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Job description: A Research Assistant / Associate position, funded by a Wellcome Trust Collaborative Award in Science, is available in the research group of Prof. Dylan Childs. The grant is led by
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PhD Opportunity: Multi-Omic Integration for Precision Oncology; Transform cancer care through genomics, data science, and translational biology. Swansea University are pleased to offer a fully
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This PhD project aims to reduce—by approximately an order of magnitude—the time and computational effort required to evaluate point-defect energetics in technologically important mixed-anion
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collaboration with Innospec, aims to investigate how different catalysts influence CNT growth, structure, and performance. Specifically, the study will compare different CNT catalysts, evaluating their impact on
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screens. Nature Reviews Molecular and Cell Biology 2023. 24, 477-494. Awwad, S.W., Doyle, C., Coulthard, J., Bader, A.S., Gueorguieva, N., Lam, S., Gupta, V., Belotserkovskaya, R., Tran, T-A
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in leaf size, shape and surface texture, all species have an astonishing ability to self-right when flipped over. Preliminary data indicate that drastic differences in surface wettability between