BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20210402T160553Z
LOCATION:
DTSTART;TZID=America/New_York:20201109T090000
DTEND;TZID=America/New_York:20201109T200000
UID:submissions.supercomputing.org_SC20_sess280_job136@linklings.com
SUMMARY:Computational Mathematician/Statistician
DESCRIPTION:Job Posting\n\nComputational Mathematician/Statistician\n\n\n\
 nPosition Description:	\nThe Mathematics and Computer Science Division at 
 Argonne National Laboratory seeks well-prepared candidates in applied math
 ematics, numerical analysis, optimization, numerical software, and statist
 ics for multiple positions at career levels from postdoctoral researchers 
 to senior staff researchers. The appointment level will be commensurate wi
 th experience.\n\nThe positions will address software/algorithm developmen
 t and/or theory in areas of interest to the applied mathematics, numerical
  software, and statistics group. Candidates should have expertise in one o
 r more of the following areas:\n\nNonlinear optimization, including mixed-
 integer, multiobjective, stochastic/robust, PDE-constrained, simulation-ba
 sed, dynamics, derivative-free, and parallel/concurrent optimization\nMach
 ine learning, data analysis, applied statistics, and uncertainty quantific
 ation\nStatistical inference and analysis, sampling, spectral estimation\n
 Stochastic processes, stochastic differential equations\nData assimilation
 , inverse problems\nHigh-order methods for PDEs/CFD including spectral ele
 ment methods\nNumerical linear algebra focusing on highly scalable precond
 itioners including matrix-free methods\nNumerical methods for ordinary and
  partial differential equations including error estimators and adjoints\nA
 utomatic/algorithmic differentiation\nQuantum information sciences, includ
 ing quantum computing, networking, and simulation\nThis is an Evergreen jo
 b posting which allows candidates to apply once to be considered for multi
 ple job requisitions; you may be asked to apply to a specific job posting 
 in the future.\n\nPosition Requirements:	\nApplicants should have a maste
 r’s or doctorate degree in computer science, mathematics, operations resea
 rch, statistics, or a related discipline.\nApplicants should have document
 ed and comprehensive expertise, commensurate with their experience, in com
 putational mathematics, computational science, or numerical libraries.\nPr
 ogramming experience in C, Python, Fortran, or another programming languag
 e is desirable; experience with parallel computing is desirable.\nOpenings
  are available immediately, but there is flexibility in start dates for hi
 ghly qualified candidates. More information on applied mathematics, numeri
 cal software, and statistics work at Argonne may be found at https://www.a
 nl.gov/mcs/lans. Feel free to contact members of the LANS group directly b
 y email with specific questions.\n\nAbout Argonne: Argonne is a multidisci
 plinary science and engineering research center, where world-class researc
 hers work alongside experts from industry, academia, and other government 
 laboratories to address vital national challenges in clean energy, environ
 ment, technology, and national security. We pursue big, ambitious ideas th
 at redefine what is possible. Our pursuit of groundbreaking discoveries pu
 shes the boundaries of fundamental science, applied science, and engineeri
 ng to solve complex challenges and develop useful technologies that can tr
 ansform the marketplace and change the world.\n\nRegistration Category: Te
 ch Program Reg Pass, Workshop Reg Pass, Tutorial Reg Pass, Exhibits Reg Pa
 ss
END:VEVENT
END:VCALENDAR

