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UID:submissions.supercomputing.org_SC20_sess280_job132@linklings.com
SUMMARY:Postdoctoral Appointee - Data Science and Learning for X-ray Scien
 ce
DESCRIPTION:Job Posting\n\nPostdoctoral Appointee - Data Science and Learn
 ing for X-ray Science\n\n\n\nPosition Description:	\nThis position will de
 velop artificial intelligence (AI) and machine learning (ML) for DOE Scien
 tific User Facilities. In particular, the project aims to develop AI/ML mo
 dels that have been trained to detect specific features in X-ray detector 
 data at the Advanced Photon Source (APS) at Argonne and Linac Coherent Lig
 ht Source (LCLS) at SLAC. Because such models can run at high speeds, than
 ks to advances in AI streaming inference accelerators, it becomes feasible
  to extract salient information from in-flight data, in real time, and thu
 s both enabling fast feedback and reducing downstream computational burden
 . Conduct cutting-edge research in data science and deep learning applied 
 to scientific problems in X-ray science. Plays a key roles in developing p
 hysics-based AI/ML models, developing workflow building blocks and impleme
 nt high-speed training on data center AI systems (e.g., Cerebras CS-1 ML a
 ccelerator and Argonne's Aurora exascale supercomputer), end-to-end model 
 training workflows and explore AI accelerators for simulation applications
 .\n\nPosition Requirements:	\nRecent PhD in a computer science, physical 
 sciences or engineering or related field.\nComprehensive experience progra
 mming in one or more programming languages, such as C, C++, and Python.\nE
 xperience with machine learning methods and deep learning frameworks, incl
 uding tensorflow, pytorch.\nSoftware development practices and techniques 
 for computational and data-intensive science problems. \nExperience with X
 -ray science techniques (e.g., crystallography).\nExperience and skills in
  interdisciplinary research involving computer and material scientists.\nE
 xperience on applied machine learning (e.g., successful projects that used
  ML to resolve scientific problems).\nExperience with high-performance com
 puting and/or scientific workflow.\nAbility to provide project leadership.
 \nExceptional communication skills, ability to communicate effectively wit
 h internal and external collaborators and ability to work in team environm
 ent\nAbility to model Argonne’s Core Values: Impact, Safety, Respect, Inte
 grity, and Teamwork.\n\nRegistration Category: Tech Program Reg Pass, Work
 shop Reg Pass, Tutorial Reg Pass, Exhibits Reg Pass
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