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UID:submissions.supercomputing.org_SC23_sess503_job113@linklings.com
SUMMARY:Postdoc - Scientific Computing (JR100591)
DESCRIPTION:Brookhaven National Laboratory (BNL) is a scientific, extreme 
 scale Data Laboratory in the U.S., New York State, Long Island. We have a 
 lively, fast-growing data science research program at BNL, with a specific
  focus on the challenges presented by the analysis, interpretation, and us
 e of data at extreme scales and in real-time. The data science program is 
 accompanied by significant computational modeling research effort, in supp
 ort of the design, planning, analysis, and interpretation of both laborato
 ry and computer experiments and their results. The Computational Science I
 nitiative (CSI - https://www.bnl.gov/compsci/) provides a laboratory-wide 
 umbrella for these activities, bringing together computer scientists, appl
 ied mathematicians, and domain scientists to carry out leading-edge resear
 ch, convert research results into practical solutions that advance domain 
 science, and provide the necessary computing infrastructure services and t
 raining to support efficient operation. The position will reside within CS
 I’s Computing for National Security group, which conducts research on syst
 em and processor architectures for data-intensive computing, involving met
 hods such as modeling and simulation, surrogate and reduced order modeling
 , optimal experimental design, uncertainty quantification, decision making
  under uncertainty, extreme-scale data analysis, and scientific machine le
 arning. Many of the activities involve use of advanced computing systems a
 t different levels of technology maturity from prototype to large-scale sy
 stems.\n\nPosition Description:\n\nThe Computing for National Security Gro
 up of the Computational Science Initiative (CSI) at Brookhaven National La
 boratory (BNL) invites exceptional candidates to apply for a post-doctoral
  research associate position in computer architecture, machine learning, a
 nd scientific computing. This position offers a unique opportunity to cond
 uct research in emerging interdisciplinary research problems at the inters
 ection of computer architecture, machine learning, and high-performance co
 mputing (HPC) with applications in diverse scientific domains of interest 
 to BNL and the Department of Energy (DOE). Topics of specific interest inc
 lude: (i) modeling and simulation of novel domain specific accelerators an
 d/or memory technologies; (ii) data-driven/machine-learning-based modeling
  and simulation technologies and tools. The position includes access to wo
 rld-class HPC resources. such as the BNL’s Advanced Computing Laboratory a
 nd other computational resources, and DOE leadership computing facilities.
  Access to these platforms will allow computing at scale and will ensure t
 hat the successful candidate will have the necessary resources to solve ch
 allenging DOE problems of interest.\n\nThis program provides full support 
 for a period of two years at BNL with possible extension. Candidates must 
 have received a doctorate in computer science, computer engineering, or a 
 related field (e.g., electrical engineering, physics) awarded within the l
 ast 5 years. This 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\nDesign and car
 ry out original research in modeling and simulation of novel domain specif
 ic accelerators and/or memory technologies for scientific computing and/or
  emerging device technologies.\nResearch and implement new solutions for r
 esilient computing in distributed, data-intensive workflows in use at lead
 ing DOE experimental facilities.\nDevelop, implement, and utilize data-dri
 ven/machine-learning-based approaches for computer system modeling and sim
 ulation.\nCollaborate with scientists within and outside of Brookhaven Nat
 ional Laboratory.\nDevelop research ideas into actionable research strateg
 ies and programs.\nPresent research progress and outcome at internal meeti
 ngs and external conferences/workshops.\nPublish research findings in peer
 -reviewed journals or conference proceedings.\n\n
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