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evaluating predictive time series models and using R to manage, analyse and visualise data including developing re-usable code in collaborative projects using platforms such as Github is also essential
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research databases (including REDCap) demonstrated high-level proficiency in R (or equivalent statistical coding environment) for analysis of large, complex datasets demonstrated experience working with
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 22 hours ago
Organization U.S. Department of Energy (DOE) Reference Code DOE-STP-OP-2025-0001 How to Apply Click on Apply below to start your application. Description The U.S. Department of Energy (DOE) Science
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evaluating predictive time series models and using R to manage, analyse and visualise data including developing re-usable code in collaborative projects using platforms such as Github is also essential
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Max Planck Institute for Medical Research, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 3 months ago
Job Code: 03/2026 Job Offer from January 16, 2026 The Max Planck Institute for Medical Research is seeking a Postdoc/project engineer (m/f/div) for the Department of Cellular Biophysics. Your Tasks
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develop new techniques for optimisation/modification of the laser pulse parameters, such as pulse focusing, filamentation, chirping, and temporal compression. Validate computational codes and implement
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., pyTorch, TensorFlow) Proven publication record in relevant fields Familiarity with high-performance computing environments Familiarity with using and adapting open-source AI models and code repositories
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abilities, cultures, sexual orientation, and genders. Pre-employment safety and suitability checks: In accordance with the National Higher Education Code to Prevent and Respond to Gender-based Violence
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. Maintain reproducible code and documentation. Conduct simulation experiments using driving simulators or other software tools to test and validate algorithms. Perform physical experiments on robotic and
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targeted drugs. · Studying the genetic and epigenetic basis of GI cancers (e.g., aberrant DNA methylation, histone modifications, and non-coding RNAs). · Understanding the biological