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Field
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, transfer learning, federated learning, data integration, algorithmic fairness, survival analysis, and methods for heterogeneous and multi-source data. Training Environment and Career Development
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force fields (MLFFs) that combine state-of-the-art equivariant neural network architectures with robust, well-calibrated uncertainty estimates. These models will enable fully automated active learning in
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"Advancing the Study of Social Isolation, Loneliness, and Cognitive Functioning in Older Adults Through the Use of Integrative Data Analysis.” As a postdoctoral associate, you will focus primarily on data
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stakeholder analysis, using methods such as literature review, qualitative interviews, and network analysis to identify key actors, trust flows, and governance dynamics. Building on these insights and those
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workforce demands and develop networks to ensure a constant flow of qualified candidates using various tools (e.g., social media, career fairs, agencies, recruiting vendors). Attend talent strategy meetings
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analysis methods for realistic CO2 electrocatalysis, with a focus on parallel investigations and accelerated aging. Dissect degradation processes of CO2 electroreduction catalyst, electrodes, and ionomer
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valued: • Qualitative and/or mixed-methods research (e.g., interviews, case studies, Delphi studies, thematic analysis), with proficiency in NVivo or similar qualitative data analysis software
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at an academic level in English are required. Experience in modeling biodiversity and ecosystem services, GIS, inter- and transdisciplinary collaboration, and quantitative knowledge synthesis (meta-analysis
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project reports and present findings. • Provide educational sessions and professional learning opportunities for CETE staff and project partners. • Engage in national professional networks related to AI in
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
insights through iterative, hypothesis-driven computational analysis. 2) Characterizing the landscape of structural mutations—including intragenic rearrangements (IGRs)—across cancer types and modeling