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applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a
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scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) novel development of deep learning ML models and adaptation of existing ones
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Abilities: Experience with topological quantum materials High pressure processing including spark plasma synthesis Experience with dilution refrigerators Synchrotron based materials characterizations
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investigators. Position Requirements Ph. D. in theoretical or physical chemistry, or a related field Extensive experience in one or more of the following areas: Computational modeling of homogeneous
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Knowledge, Skills, and Abilities: PhD in Chemistry, Physics, Biophysics, Biology, Biochemistry or Structural Biology. Proven ability to optimize peptide, protein or nucleic acid crystallization systems. Basic
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life – including the explosion of large language model (LLM) releases. BNL is engaged in numerous research efforts that employ NLP techniques for science and security applications and uses
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year depending on performance and availability of funds. Candidates must have received a Ph.D. by the commencement of employment. BNL policy requires that after obtaining their PhD, eligible candidates
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industry, education, and public life – including the explosion of large language models (LLMs). BNL is engaged in numerous research efforts that employ NLP techniques for science and security applications
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses