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UID:submissions.supercomputing.org_SC23_sess503_job114@linklings.com
SUMMARY:Postdoc - Machine Learning (JR100553)
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). Topics of particular interest include: (i) novel developm
 ent of deep learning ML models and adaptation of existing ones for scienti
 fic and security applications; (ii) ML models for natural language process
 ing (NLP), including Large Language Models (LLMs) and multi-modal, multi-t
 ask Foundation Models; and (iii) techniques supporting stakeholders and en
 d-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. Ac
 cess to these platforms will allow computing at scale, and together with a
 ccess to unique data sources, will ensure that the successful candidate ha
 s the necessary resources to solve challenging DOE problems of interest. T
 he successful candidate will join a growing research group with diverse ex
 pertise and projects spanning the full breadth of BNL’s and the DOE’s miss
 ions. This post-doc position presents a unique chance to conduct interdisc
 iplinary collaborative research in BNL programs with a highly competitive 
 salary.\n\nEssential Duties and Responsibilities:\n\nConduct research in M
 L and NLP for various problems relating to scientific discovery, workflow 
 acceleration, and national security.\nImplement, adapt, and evaluate ML an
 d NLP algorithms for scientific and security applications.\nWork in interd
 isciplinary collaborations with subject matter experts on various aspects 
 of scientific data generation and processing, and methods evaluation.\nFor
 mulate own high-quality research ideas and directions in collaboration wit
 h mentors in the group.\nCommunicate research progress, challenges and ach
 ievements, and engage within and beyond the group on new potential collabo
 rations.\n\n
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