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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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Apply Now How to Apply To be considered for this position, applicants should submit their materials to Interfolio https://apply.interfolio.com/179185 Cover letter (1-2 pages) which includes a
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fluid dynamics. The successful candidate will be expected to work on all or a subset of the above topics, be proficient in working with large data-sets (observational or numerical), machine learning, and
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the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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Cornell University, Electrical and Computer Engineering Position ID: Cornell-ECE-POSTDOC [#31375] Position Title: Position Type: Postdoctoral Position Location: Ithaca, New York 14853
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics