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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 8 days ago
their own agenda within these project(s). We are seeking creative, self-driven, and collaborative scholars. Strong quantitative analysis skills are required, as is knowledge of R, Stata, or Python. We provide
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The Energy Systems Analysis Group (https://acee.princeton.edu/research/energy-systems-analysis-group/) at the Andlinger Center for Energy and the Environment invites applications for postdoctoral
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Learning for Cybersecurity, AI for 3D Imaging, Recommender Systems, Quantum AI, Blockchain AI, AI for Autonomous Systems Foundational courses (for Connect pathway students): Python programming, Mathematical
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fluency in at least one programming language (e.g. Python, R, or similar). Background in cancer biology preferred Problem Solving Works with a team to troubleshoot computational analysis Decision Making
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for our multidisciplinary research focusing on transportation safety and mobility (http://umtri.umich.edu ). Established in 1965, we maintain the highest standards of excellence as we conduct research
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LIST? Check our website: https://www.list.lu/ How will you contribute? The Post-Doc researcher will develop, implement, and apply advanced ways in inverting a radiative transfer model for forest trait
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working with electronic health record data and familiarity with programming in R, Python, or SQL. Above all, attention to detail and the ability to clearly and concisely convey nuanced technical information
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communication skills in written and spoken English. Proficiency in programming with high-level languages like Python. The following will strengthen your application: Familiarity with open quantum systems
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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented