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Investigator, Dr. Hwan Kim , in the department of Microbiology and Immunology. Please visit the Kim Laboratory to learn more about our department/research. The incumbent will conduct research and ensure
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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conceptual understanding and collaborative inquiry, and institutional change via reform efforts inspired by Science Education Research and/or the Learning Sciences. The Post Doctoral Scholar will be expected
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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technology (PAT) adds cost, time, and introduction of human error. This project aims to develop a novel, label-free microbial detection method, coupled with machine learning tailored for cleaning validation
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enable physics-informed AI agents that accelerate discovery in catalysis science. The successful candidate will focus on the development and application of machine learning and AI approaches to analyze
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of active learning pedagogy that strengthens ‘systems-thinking’ throughout the cross-college Environment & Sustainability (E&S) major. This is a full-time position, based at the Ithaca campus. We expect
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Investigator, Dr. Hwan Kim , in the department of Microbiology and Immunology. Please visit the Kim Laboratory to learn more about our department/research. The incumbent will conduct research and ensure
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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). Deep learning has been used to perform this mapping from UAV (Batista et al., 2025; Chudasama et al., 2024; Lambert et al., 2025) or satellite imagery (Mattéo et al., 2021), at both very high resolution