BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:America/Chicago X-LIC-LOCATION:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230124T171527Z LOCATION: DTSTART;TZID=America/Chicago:20221116T100000 DTEND;TZID=America/Chicago:20221116T150000 UID:submissions.supercomputing.org_SC22_sess488_job122@linklings.com SUMMARY:Machine Learning Postdoctoral Fellow DESCRIPTION:Job Posting\n\nMachine Learning Postdoctoral Fellow\n\n\n\nMac hine Learning Postdoctoral Fellow - 94786\nDivision: NE-NERSC\n\nThe Natio nal Energy Research Scientific Computing Center (NERSC, https://www.nersc. gov/about/) at Berkeley Lab seeks highly motivated Machine Learning Postdo ctoral Fellows to join the NERSC Exascale Science Application Program (NES AP, https://www.nersc.gov/users/application-performance/nesap/). NESAP pos tdocs collaborate with scientific teams to enable the solution of deep, me aningful problems across all program areas funded by the Department of Ene rgy Office of Science (https://science.energy.gov/).\n\nThe Challenge: Ena bling machine learning at scale on energy-efficient supercomputers.\n\nNES AP for Learning (N4L): Machine Learning (ML) and Deep Learning (DL) are po werful approaches to solving complicated classification, regression, and p attern recognition problems. N4L focuses on developing and implementing cu tting-edge ML/DL solutions to improve scientific discovery potential on ex perimental or simulation data or improving HPC applications by replacing p arts of the software stack or algorithms with ML/DL solutions.\n\nTo enabl e new discoveries through simulation, data analytics, and ML/DL, NERSC beg an deploying “Perlmutter,” a Cray supercomputer, in 2021. Perlmutter, a sy stem optimized for science, is a heterogeneous system including current-ge neration AMD CPUs and NVIDIA GPUs. It also has a high-speed interconnect a nd an all-flash file system.\n\nAs a NESAP Fellow, you will be a part of a multidisciplinary team composed of computational and domain scientists wo rking together to develop machine learning approaches that run on the Perl mutter system and produce mission-relevant science that pushes the limits of HPC. You will carry out these efforts in collaboration with a project P I and team members, with the support of NERSC and vendor staff. \n\nNESAP has established a track record of enabling its postdocs to pursue careers in data science, HPC, and scientific computing both in industry and at nat ional labs.\n\nWhat You Will Do:\n• Working with domain experts and NERSC staff, develop, adapt, and optimize state-of-the-art ML/DL models to solve scientific problems on HPC systems.\n• Disseminate results of research ac tivities through refereed publications, reports, and conference presentati ons. Ensure that new methods are documented for the broader community, NER SC staff, vendors, and NERSC users.\n• Participation in postdoctoral caree r and science enrichment activities within the Berkeley Lab Computing Scie nces Area is encouraged.\n\nWant to learn more about Berkeley Lab's Cultur e, Benefits and answers to FAQs?\nPlease visit: https://recruiting.lbl.gov /\n\nNotes:\n• This is a full-time, 2 years, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, conti nuing availability of funds and ongoing operational needs. You must have l ess than 4 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.\n• There are multipl e openings for this position.\n• This position is represented by a union f or collective bargaining purposes.\n• Salary will be predetermined based o n postdoctoral step rates.\n• This position may be subject to a background check. Any convictions will be evaluated to determine if they directly re late to the responsibilities and requirements of the position. Having a co nviction history will not automatically disqualify an applicant from being considered for employment.\n• Work will be primarily performed at Lawrenc e Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.\n\nHow To Apply\n Apply directly online at http://50.73.55.13/counter.php?id=243105 and foll ow the on-line instructions to complete the application process.\n\nBased on University of California Policy - SARS-CoV-2 (COVID-19) Vaccination Pro gram and U.S Federal Government requirements, Berkeley Lab requires that a ll members of our community obtain the COVID-19 vaccine as soon as they ar e eligible. As a condition of employment at Berkeley Lab, all Covered Indi viduals must Participate in the COVID-19 Vaccination Program by providing proof of Full Vaccination or submitting a request for Exception or Deferra l. Visit covid.lbl.gov (https://covid.lbl.gov/) for more information.\n\nB erkeley Lab is committed to Inclusion, Diversity, Equity and Accountabilit y (IDEA, https://diversity.lbl.gov/ideaberkeleylab/) and strives to contin ue building community with these shared values and commitments. Berkeley L ab is an Equal Opportunity and Affirmative Action Employer. We heartily we lcome applications from women, minorities, veterans, and all who would con tribute to the Lab's mission of leading scientific discovery, inclusion, a nd professionalism. In support of our diverse global community, all qualif ied applicants will be considered for employment without regard to race, c olor, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.\n\nEqual Opportunity and ID EA Information Links:\nKnow your rights, click here (https://www.dol.gov/a gencies/ofccp/posters) for the supplement: Equal Employment Opportunity is the Law and the Pay Transparency Nondiscrimination Provision (https://www .dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA5 08c.pdf) under 41 CFR 60-1.4. END:VEVENT END:VCALENDAR