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Field
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research-operational partnerships and learning about systems involving forest fuels and fire emissions modeling. They will gain experience with modeling, coding, and database management in support of a
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researchers engaged in a Defense Health Agency project on spatial orientation modeling and disorientation mitigation. The research fellow will participate in generating a report for the Spatial Orientation
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of an undergraduate degree in a related field. Training in causal inference and/or machine learning methods Effective oral and written communication skills Outstanding academic credentials and intellectual creativity
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in this position will conduct/lead applied as well as fundamental research in physics-informed Artificial Intelligence (AI) and Machine Learning (ML) methodologies enabling digital twin functionalities
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and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In addition to your research leadership, you will play a
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experiments to validate findings from the omic studies. The desired skill set is split between dry lab (bioinformatics) and wet lab (basic science/animal models). Additionally, the individual will be required
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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. The postdoctoral fellow will lead efforts to develop novel machine learning models for integrating omics datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic) with relevant molecular pathways
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agricultural impacts, and guiding policies to enhance regional resilience to climate-driven natural hazards. Subsequent efforts focus on employing a physics-informed machine-learning model, tailored
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to the advancement of AI applications in biological sciences. This role presents a unique opportunity to work with pangenomic datasets while exploring the application of Large Language Models (LLMs) and machine