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DTSTAMP:20250626T233642Z
LOCATION:Exhibit Hall A3 - Job Fair Inside
DTSTART;TZID=America/New_York:20241119T103000
DTEND;TZID=America/New_York:20241119T150000
UID:submissions.supercomputing.org_SC24_sess543_job101@linklings.com
SUMMARY:Talent Acquisition Lead
DESCRIPTION:We are seeking a highly motivated Postdoctoral Appointee with 
 a strong background in AI/ML, specifically in the development and applicat
 ion of Large Language Models (LLMs) tailored for scientific use cases. Thi
 s position is focused on advancing the capabilities of LLMs to address com
 plex problems within specific scientific domains, with an emphasis on clim
 ate risk assessment and analysis.\n\nAs part of a multidisciplinary team, 
 the Postdoctoral Appointee will work at the intersection of AI/ML, climate
  science, and high-performance computing. The candidate will develop LLMs 
 specifically designed to understand, process, and analyze scientific data 
 specifically related to climate risks, including extreme weather events, l
 ong-term environmental changes, and their impact on infrastructure and eco
 systems.\n\nThis role requires not only expertise in LLMs and machine lear
 ning but also an understanding of the unique challenges posed by scientifi
 c data, which often includes large-scale numerical datasets, complex simul
 ations, and multimodal information. This position offers the opportunity t
 o work with some of the world’s most advanced computing resources, includi
 ng Exascale supercomputers, and to collaborate with leading experts across
  a range of disciplines. Those who are passionate about using cutting-edge
  AI to address some of the most critical challenges facing our planet are 
 encouraged to apply.\n\nKey Responsibilities:\n\n• Optimize Retrieval-Augm
 ented Generation (RAG) techniques to improve the relevance and contextual 
 accuracy of LLM-generated content.\n\n•Explore and apply multimodal LLMs c
 apable of effectively processing and integrating scientific data from dive
 rse sources, including numerical tables, text, and images.\n\n• Design and
  implement LLM guardrails to enhance the reliability and accuracy of model
  outputs in scientific applications.\n\n• Develop and refine automatic eva
 luation techniques that enable the continuous assessment of LLM performanc
 e, particularly in terms of accuracy, relevance, and robustness in scienti
 fic contexts.\n\n• Implement conformal prediction and uncertainty quantifi
 cation techniques to provide reliable risk assessments and uncertainty est
 imates in LLM applications.\n\n• Present research findings at national and
  international conferences.\n\nRegistration Category: Tech Program Reg Pas
 s, Workshop Reg Pass, Tutorial Reg Pass, Exhibits Reg Pass\n\nCountry: USA
 \n\nCompany: Argonne National Laboratory\n\nIn-Person / Remote: In-person\
 n\nPart Time / Full Time: Full Time\n\n
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