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Field
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efforts in the group are: 1) generating large and diverse antibody sequence-affinity datasets to train affinity-aware antibody design models, 2) generating diverse sequence-function maps for enzymes
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on the seismic performance of structural components. - Develop consequence models for various structural systems to demonstrate the real-world application of the unified database. - Design and
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Ph.D. in a relevant field by the time of appointment. The postdoc will be based at the Harvard University Herbaria (HUH) and will have access to all the resources available through the Department of OEB
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-efficiency requirements at the energy edge. Further, you will incorporate compliance-by-design AI architectures and models and validate our solutions across key energy use cases such as energy market
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15th July 2026 Languages English English English The Department of Natural History has a vacancy for a Postdoctoral Fellow in hologenomic/multi-omic modelling Apply for this job See advertisement
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will be required to provide a mass value and model-based uncertainty using multiple methods to cross-check. These methods may include mass spectrometry and inertial balance development and prototype
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transcriptomics and single-cell sequencing. Perform in vivo experiments in transgenic mouse models of liver injury. Develop and troubleshoot research protocols. Analyze experimental data and interpret results
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Posting Information Posting Details Department SOP-DPET - 452200 Posting Open Date 08/22/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title Postdoc
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satellite imagery are fused with land-surface models using data assimilation. The goal is to develop an adaptive experimental design frame-work for the observing system to guide ongoing measurement campaigns
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electricity market design Transportation systems modeling and optimization Responsibilities: Postdoctoral fellows will: Develop, train, validate, and benchmark AI/ML models using large-scale, real-world