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UID:submissions.supercomputing.org_SC23_sess503_job116@linklings.com
SUMMARY:Postdoc -  Machine Learning (JR100600)
DESCRIPTION:The Machine Learning Group of the Computational Science Initia
 tive (CSI) at Brookhaven National Laboratory (BNL) invites exceptional can
 didates to apply for a post-doctoral research associate position in machin
 e learning (ML). This position offers a unique opportunity to conduct both
  basic and applied research in concert with collaborators working on diver
 se scientific and security problems of interest to BNL and the Department 
 of Energy (DOE).\n\nTopics of particular interest include: (i) novel devel
 opment of deep learning ML models and adaptation of existing ones for scie
 ntific and security applications; (ii) ML models for natural language proc
 essing (NLP), including Large Language Models (LLMs) and multi-modal, mult
 i-task Foundation Models; and (iii) techniques supporting stakeholders and
  end-users of applied ML methods, including uncertainty quantification (UQ
 ), interpretability and explainability (XAI), and visualization techniques
 .\n\nThe position provides access to world-class computing resources, such
  as the BNL Institutional Cluster and DOE leadership computing facilities.
  Access to these platforms will allow computing at scale, and together wit
 h access to unique data sources, will ensure that the successful candidate
  has the necessary resources to solve challenging DOE problems of interest
 . The successful candidate will join a growing research group with diverse
  expertise and projects\nspanning the full breadth of BNL’s and the DOE’s 
 missions.\n\nThis post-doc position presents a unique chance to conduct in
 terdisciplinary collaborative research in BNL programs with a highly compe
 titive salary.\n\nEssential Duties and Responsibilities:\n\n● Conduct rese
 arch in ML and NLP for various problems relating to scientific discovery, 
 workflow acceleration, and national security.\n● Implement, adapt, and eva
 luate ML and NLP algorithms for scientific and security applications.\n● W
 ork in interdisciplinary collaborations with subject matter experts on var
 ious aspects of scientific data generation and processing, and methods eva
 luation.\n● Formulate own high-quality research ideas and directions in co
 llaboration with mentors in the group.\n● Communicate research progress, c
 hallenges and achievements, and engage within and beyond the group on new 
 potential collaborations.\n\n
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