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Field
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stakeholders in academic and government as it pertains to field research. Experience with machine learning (ML) approaches to analyzing data. experience in student leadership or mentorship roles. Experience
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have recently received their Ph.D. in sociology, human geography, or a related discipline; Demonstrated expertise in spatial analysis, machine learning, or other advanced quantitative methods
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experience in machine and/or deep learning applied to geospatial data. Demonstrated experience in the use of HPC and handling of very large datasets. Experience in software development and contribution
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School of Engineering and Applied Sciences at Harvard University invites applications for a postdoctoral position in robot learning, beginning in September 2025, or soon thereafter. The position is for one
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position under the supervision of Dr. Chris Smith. Our new lab in Evolution, Ecology, and Behavior investigates machine learning approaches for population genetics. Our research integrates custom neural
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under uncertainty, and machine-learning-enhanced autonomy for Advanced Air Mobility. The anticipated start date is Fall 2025 or Spring 2026. Responsibilities include: Conducting advanced research in GNC
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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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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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-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In
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repeated for a database of events covering different sea ice types, conditions, locations, and rates of ice deformation (from docile to violent). Machine learning techniques will then be used to find a