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combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO) and General Observer (GO) programs
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proficiently and timely, and analyzing data professionally. The scientists are expected to handle multiple projects, to write scientific papers and reviews, and to present work in scientific conferences
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, conducting, analyzing, and compiling data from a large number of AML cases. Qualifications Required: PhD in Clinical Genetics or equivalent Documented experience in multimodal single-cell analysis of leukemia
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for cell activity, flow cytometry, cell sorting (e.g. BONCAT-FACS). • Quantitative skills in analysis of large datasets, for example: to integrate biological/genomic data with biogeochemical measurements
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at the field, drone, and satellite scale. Big data analysis including processing and analyzing EMIT hyperspectral datacubes over large spatial domains such as the Colorado Plateau and even global. Presenting
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Leinweber Institute for Theoretical Physics (LITP). Potential research topics include: inflationary cosmology and its connection to late-time observables; large-scale structure theory and analysis; cosmic
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individual who will be responsible for recruitment and data collection in the final work package of the ICALD programme: a randomised trial with stepped wedge design evaluating the delivery and implementation
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statistical and computational methods designed to use “big data” and to address questions of direct or indirect relevance to common complex diseases and disorders. The appointee will join the group of Professor
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satisfactory performance and funding availability. Applicants should possess a PhD degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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this project, we will develop neural diffusion techniques to design materials with targeted optical properties, scaling to large systems through efficient representations and GPU parallelization. We will also