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, Python, Stata, or other statistical programming environments Strong interest in research related to ultra-processed foods, addictive processes, eating behavior, or food policy The salary range is $65,000
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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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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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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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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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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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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