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Environment Overview Responsibilities: The Postdoctoral Associate will receive mentorship from the Director and the Institute’s Fellows. The Associate must maintain an active research agenda, teach, and
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methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages (e.g. R, Python) and an
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
to antibiotics and host-like conditions. • Develop and apply statistical or machine-learning methods for interpreting single-cell and genomic datasets. • Work closely with wet-lab scientists to design perturbation
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experience in computational and machine learning approaches for use in the quantitative measurement of human learning and its associated behaviors and motivational constructs. A strong applicant will hold Ph.D
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work and funding. The Postdoctoral Associate will collaborate with instructors of Environment and Sustainability (E&S) core courses to: develop and integrate active-learning strategies that strengthen
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● Experience with machine learning, AI, statistical modeling, or data analysis, demonstrated through publications, projects, or dissertation work ● Track record of completing research projects, as
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and continued funding. Fellows in the postdoctoral track are not required or expected to teach, but if they wish, they may have the option of teaching. Teaching opportunities are subject to sufficient
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technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring
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modeling approaches-including machine learning (ML), hydrologic and energy systems simulations, and scenario forecasting-to evaluate dynamic energy-water futures and resilience strategies for diverse Idaho
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experience with managing, processing, and analyzing large datasets and strong programming skills especially in Python. ● Experience working with transmission power flow and machine learning models. ● Strong