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to junior researchers, graduate students, and lab technicians, fostering an environment of learning and collaboration. Statement In this position, the Postdoctoral Fellow will frequently encounter complex
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University of California, Los Angeles | Los Angeles, California | United States | about 21 hours ago
systems and the novel in vitro culture system by engineering 3D hydrogels. Postdoctoral Scholars at the University of California are exclusively represented by the United Automobile, Aerospace, and
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. · Experience in collaborations with experimental organic and biocatalysis research groups. · Experience in using machine learning tools in chemical research. · Expertise in using a variety of
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, multiscale modeling, molecular simulation code/software (e.g., LAMMPS, GROMACS), machine learning. Prior experience with applying simulations to biomolecular systems is a plus but not required. Applicants
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members across electrical engineering, earth science, signal processing, and machine learning to ensure methodological rigor and practical relevance. Qualifications: -Ph.D. in Electrical Engineering
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. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
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are also developing novel machine learning methods to improve risk gene prediction and variant interpretation. This role will focus on the analysis of large-scale human genetics, scRNAseq, and proteomics
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plan that supports you and your family’s wellbeing and allows you to be a part of the life of a vibrant and active college campus. To learn more, go to Baylor Benefits & Advantages. Explore & Engage
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, California 95616, United States of America [map ] Subject Areas: Physics / Astronomy , Astrophysics Computational , Machine Learning Salary Range: Starting at $71,491/year or higher depending on experience
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identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision