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our work, grow professionally, and aim for the extraordinary Learn more about Financial Administration (harvard.edu) and our eight reporting units. (https://finance.harvard.edu/) Job Description For
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provides biostatistical and data management support for research projects. Biostatistician will be responsible for data analysis and visualization for scientific publications and grant proposals
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“HORIA HULUBEI” National Institute for Research and Development in Physics and Nuclear Engineering | Romania | 14 days ago
/programming skills for text editing and data analysis (e.g., : Microsoft Office Suite, C++, Matlab, IDL, Python, or other programs for data and image processing); A high level of intellectual curiosity
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The incumbent independently conducts complex financial analysis and reconciliation of contracts and grants expenditures in excess of $5 million, ensuring adherence to sponsored award requirements
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, firewalls and common networking protocols (HTTP, DNS, SMB, etc.) Experience with Regex and at least one common scripting language (PERL, Python, PowerShell) Experience with an enterprise SIEM platform
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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Recherche Scientifique (CNRS). The fellow will work on the project's “Decarbonization” pillar, with a focus on the analysis of the spatio-temporal dynamics of peri-urban forests, using LiDAR remote sensing
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postdoctoral researcher to join our group at the Institute of Genetics and Development in Rennes (IGDR), France (https://igdr.univ-rennes.fr/en ). Research in the Cyclochondria team led by Giulia Bertolin (https
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LEO contract can be found at https://hr.umich.edu/sites/default/files/um-leo-agreement-2021-2024-final.pdf . Mission Statement PitE is an interdisciplinary program that is jointly managed between
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biology, simulation, data visualisation, bulk / single-cell / spatial RNA-sequencing analysis. Skills Terms programming; R; Python; Statistical methods; machine learning; differential expression; bulk