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UID:submissions.supercomputing.org_SC23_sess503_job139@linklings.com
SUMMARY:Postdoc - Computational Science (JR100662)
DESCRIPTION:The Applied Mathematics Group of the Computational Science Ini
 tiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional 
 candidates to apply for a post-doctoral research associate position in app
 lied mathematics, machine learning, and scientific computing. This positio
 n offers a unique opportunity to conduct research in emerging interdiscipl
 inary research problems at the intersection of applied mathematics, machin
 e learning, and high-performance computing (HPC) with applications in dive
 rse scientific domains of interest to BNL and the Department of Energy (DO
 E). Topics of specific interest include: (i) optimal decision-guided infor
 mation and data processing including data reduction; (ii) high-dimensional
  compositional workflows that involve machine learning (ML) models; (iii) 
 Bayesian inference and uncertainty quantification in scientific ML models 
 and physical systems; (iv) learning/optimization of low-dimensional latent
  feature spaces for ML surrogates. The position includes access to world-c
 lass HPC resources, such as the BNL Institutional Cluster and DOE leadersh
 ip computing facilities. Access to these platforms will allow computing at
  scale and will ensure that the successful candidate will have the necessa
 ry resources to solve challenging DOE problems of interest.\n\nThis progra
 m provides full support for a period of two years at CSI with possible ext
 ension. Candidates must have received a doctorate (Ph.D.) in applied mathe
 matics, statistics, computer science, or a related field (e.g., mathematic
 s, engineering, operations research, physics) within the past five years. 
 This post-doc position presents a unique chance to conduct interdisciplina
 ry collaborative research in BNL programs with a highly competitive salary
 . \n\nEssential Duties and Responsibilities:\n\nConduct research in variou
 s applied mathematics/machine learning problems in the context of composit
 ional workflows and surrogate or reduced modeling.\nWork in interdisciplin
 ary collaborations with applied domain scientists on various aspects of sc
 ientific data generation, processing, and compression.\nFormulate a high-q
 uality research program in collaboration with mentors in the group.\n\n
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