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Field
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with experimental collaboration to uncover complex biological mechanisms. Our interdisciplinary work draws on statistical physics, applied mathematics, and close ties with experimental labs. Current
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experience: Essential criteria PhD in neuroscience, neuroimaging or related discipline. Experience and knowledge of performing functional MRI experiments Proficiency in the use of statistical packages (e.g. R
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record of peer-reviewed publications. A background is required in computer programming (including Julia and/or C/C++), applied mathematics and statistics. Please upload your application materials via
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science. The position entails research on (1) analysis of big data using advanced statistical methods, (2) assessment of the neurocognitive effects of tobacco use, and (3) behavioral interventions
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computer programs to design experimental paradigms, analyse data and conduct advanced statistical analysis. Prior experience in running neuromodulation studies including TMS and TUS is essential. You will be
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. Programming skills in R is a requirement, and programming skills in dynamic modelling in other languages is a merit. Fluency in spoken and written English is a requirement. Qualifications: PhD degree in ecology
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PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical
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of Civil and Environmental Engineering / Chaney Lab: Perform the core of the proposed research activities including processing the remotely sensed LST to compute the spatial statistics, run the HydroBlocks
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Identification of soil invertebrates (e.g. mites, springtails, insects) using modern and classical techniques Laboratory analyses of soil properties Statistical analysis of complex ecological datasets Presentation
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interdisciplinary, and together we contribute to science and society. Your role We seek a highly motivated bioinformatician or computational biologist who is well versed in the statistical and machine learning