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to the project's open-science outputs, including containerized model workflows. Dissemination and scientific output. Publish findings in peer-reviewed journals, present at leading conferences, and contribute
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programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to
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, demonstrate your AI-native capability in any way you think is effective (e.g., a short portfolio of AI-built projects, a small prototype, GitHub repo, etc.). We value signal over formality. Where to apply
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. Present results at professional meetings, conferences, and public seminars (30%). Engage with colleagues at the Cornell Lab, external partners, and possible funders. Participate in career and professional
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or health-related disciplines, with multiple years of relevant experience. They should demonstrate strong competency in qualitative and quantitative data analysis and have a good command of written and
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cloning, confocal microscopy, next-generation sequencing, bioinformatics. Mandatory requirements: Applicants must have obtained their PhD within the past seven years; demonstrate experience in at least one
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regulation (e.g. MOFA2, MOFA+). Collaborate closely with experimental researchers. Prepare manuscripts for publication in high-impact journals. Present findings at local and international conferences
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should demonstrate strong motivation to learn these methods, Ability to work independently as well as collaboratively in a team environment, Proficiency in English (the project involves collaboration with
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Postdoctoral researcher (17 months): Ethics and Epistemology of LLMs4EU: Design, Data and Deployment
geographical and linguistic coverage. The project will develop different relevant use cases in five domains (telecommunications, science, tourism, public services, energy) to demonstrate the capacity of European
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and present experimental data. This includes applying computer analysis and simulation programs to interpret experimental data and characterize battery electrode materials and interfaces. Adapt and