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UID:submissions.supercomputing.org_SC23_sess503_job121@linklings.com
SUMMARY:Postdoc - Computational Science and Machine Learning (JR100570)
DESCRIPTION:The Machine Learning Group of the Computational Science Initia
 tive at Brookhaven National Laboratory (BNL) invites exceptional candidate
 s to apply for a post-doctoral research associate position in computationa
 l modeling and machine learning. This position offers a unique opportunity
  to conduct both basic and applied research in concert with collaborators 
 working on diverse scientific and security problems of interest to BNL and
  the Department of Energy (DOE). \n \nTopics of particular interest includ
 e: (i) first-principles modeling as applied to energy storage and (ii) sci
 entific machine learning for accelerating simulations and analyzing data. 
 The successful candidate will work with scientists across multiple other d
 epartments at BNL, including the Interdisciplinary Science Department, Cen
 ter for Functional Nanomaterials, and National Synchrotron Light Source II
 . \n\nThe position provides access to world-class computing resources, suc
 h as the BNL Institutional Cluster and DOE leadership computing facilities
 . Access to these platforms will allow computing at scale, and together wi
 th access to unique data sources, will ensure that the successful candidat
 e has the necessary resources to solve challenging DOE problems of interes
 t. The successful candidate will join a growing research group with divers
 e expertise and projects spanning the full breadth of BNL’s and the DOE’s 
 missions. \n\nThis post-doc position presents a unique chance to conduct i
 nterdisciplinary collaborative research in BNL programs with a highly comp
 etitive salary. \n \nEssential Duties and Responsibilities: \n\nConduct re
 search in electrocatalysis and chemical reaction modeling for energy stora
 ge. \n\nPerform data analysis by applying existing machine learning method
 s and novel learning algorithms. \n\nWork in interdisciplinary collaborati
 ons with subject matter experts on various aspects of scientific data gene
 ration and processing, and methods evaluation. \n\nFormulate own high-qual
 ity research ideas and directions in collaboration with mentors in the gro
 up. \n\nCommunicate research progress, challenges, and achievements, and e
 ngage within and beyond the group on new potential collaborations.\n\n
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