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model for behavior that reflects BNL Values and holds staff accountable. Required Knowledge, Skills, and Abilities: • PhD. in Chemistry, Materials Science, Physics, or other relevant field with 10 years
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foundational ML and NLP innovations. This work involves the development and applications of encoding and generative NLP models (e.g., large language models, LLMs), and includes model training, tuning and
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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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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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: Ph. D in physical chemistry or a related field and 4+ years of post-PhD relevant experience. Experience in radiation chemistry, photochemistry and familiarity with electron transfer theory
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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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Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a wide range of material parameters. The CFN develops and utilizes
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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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of the student will be the performance of power grid modeling and simulation, statistical analysis and machine learning applications in power system control or cybersecurity, and the implementation in Python and
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Abilities: PhD in a field related to computational and data sciences with fifteen plus years of broadly related experience, inclusive of ten years of demonstrated management experience, including leading