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, unit testing, CI/CD, and collaborative development. Publications in ML-for-Science, HPC, or systems venues (SC/ISC, PPoPP, IPDPS, MLSys, NeurIPS workshops). Preferred Application Materials Notes: This is
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, materials science, engineering, or a related discipline, with no more than three years of prior postdoctoral experience. Strong academic track record shown through publications and scientific presentations
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Chemistry, Physics, Materials Science, or a related field. Hands-on experience with first-principles or atomistic methods relevant to interfacial systems, including DFT, ab initio MD, enhanced sampling
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join the Workflow Readiness team as part of NERSC’s Exascale Science Acceleration Program (NESAP ). You’ll work with NERSC staff, domain scientists, and engineers from industry partners to prepare key
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to maintain laboratory equipment and instruments. Additional Responsibilities as needed: Participate in research proposal writing. What is Required: Ph.D. in Chemistry, Material Sciences, or a closely related
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electrical engineering, computer science, physics, mechanical engineering, applied math, theoretical neuroscience, or statistics. In depth experience with control theory and machine learning for analysis
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and submit the requested application materials. Note that applications will only be accepted via this Academic Jobs Online URL: https://academicjobsonline.org/ajo/jobs/30661 The following requested
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, machine learning or AI to computational modeling, simulations, and advanced data analytics for scientific discovery in materials science, biology, astronomy, environmental science, energy, particle physics
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global supply chains grow more complex and resource resilience becomes increasingly vital. This is a unique opportunity to work at the intersection of materials science, genomics, and microbial engineering
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astrophysics and cosmology research program. The Fellows will also find a rich data-science environment at UC Berkeley and LBNL, with world-expert researchers in both computer science/statistics and domain