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asset records, satellite imagery) with field investigations to identify vulnerable regions and understand surface deterioration processes. You’ll gain experience in spatial analysis, fieldwork, soil and
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intelligence (AI) and machine learning (ML)) and emerging data types (electronic health records (EHR), biobanks and disease registries, and next-generation genomics including single-cell and spatial omics
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. Applicants with experience in Bayesian modeling, spatial statistics, mathematical modeling, data integration, uncertainty quantification and/or machine learning are encouraged to apply. The postdoc will also
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integrating visual data with spatial sensors like RGBD and LiDAR. This Research Engineer (Research Technician 2) role offers the opportunity to work across the full stack of data collection, model training
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented
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for modelling various cognitive processes on a neuroscientific basis, which are tested using robots. Areas of study include perception, memory, learning, cognitive development, attention, motor control and
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Boston University Chobanian & Avedisian School of Medicine | Boston, Massachusetts | United States | 3 months ago
genetics, single-cell/nucleus RNA sequencing, proteomics, experimental design and broad knowledge of in vitro models, spatial transcriptomics, and related molecular profiling techniques. BU offers state
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, associate or full professor) in teaching and research, with an expected start date of August 2026. Applicants must hold a Ph.D. We welcome scholars whose expertise engages geographical/spatial issues across
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multidisciplinary experience in combining integrative computational immunology – data-driven, state-of-the-art single cell resolution and spatial methods, machine learning and kinetic modeling – with integrative
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sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling to evaluate the technical, environmental, and socioeconomic implications of transitioning to resilient crops in