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Institute of Standards and Technology, housed at the University of Vermont, with which CHanGE is partnered. The Center’s aims are to develop standardized methods and measurements for climate impacts and
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, Biostatistics/Statistics, Mathematics, Biomedical Engineering, or a related field by the start date. Skills: We welcome candidates grounded in one or more of the following: Generative AI/transformers, agentic AI
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technology, combining knockout and knock-in mouse models. Ideal candidates will have a MD and/or PhD in biochemistry, physiology, and nutrition. The position is part of a well-funded research program and will
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Analytics (MSA) Group in the Manufacturing Science Division (MSD), Energy Science and Technology Directorate (ESTD), at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop algorithms
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for autonomous control. Position Requirements Ph.D. completed in the past 5 years or soon-to-be completed in chemistry, chemical engineering, materials science, or a closely related field. The applicant should
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informatics, electrical engineering, or a related field Demonstrated expertise in machine learning, deep learning, and image-based modeling. A strong publication record in top-tier venues (e.g., MICCAI, CVPR
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Manufacturing (CAAAM) (caaam.unt.edu), a State of Texas funded multimillion dollar initiative with a multidisciplinary focus on further advancing the science and technology of additive manufacturing (AM). with
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the past five years or soon-to-be completed in physics, materials science, chemistry, engineering, or a related discipline. Demonstrated expertise in one or more synchrotron X-ray methods such as BCDI, XPCS
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a Ph.D. degree in Electrical Engineering, biomedical engineering, Physics or related field with 1-3 years of experience in MR pulse sequence development, and / or experience with modern reconstruction
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
quantitative skills and curiosity about complex systems. Position Overview You will design, implement, and apply new computational and statistical models to reverse-engineer causal networks from noisy, high