Postdoctoral Appointee - Data Science and Learning for X-ray Science
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Argonne National Laboratory
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Lemont, IL
SessionJob Fair
Event Type
Job Posting
TP
W
TUT
XO
TimeMonday, 9 November 20209am - 8pm EDT
Location
DescriptionPosition Description:
This position will develop artificial intelligence (AI) and machine learning (ML) for DOE Scientific User Facilities. In particular, the project aims to develop AI/ML models that have been trained to detect specific features in X-ray detector data at the Advanced Photon Source (APS) at Argonne and Linac Coherent Light Source (LCLS) at SLAC. Because such models can run at high speeds, thanks to advances in AI streaming inference accelerators, it becomes feasible to extract salient information from in-flight data, in real time, and thus 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 physics-based AI/ML models, developing workflow building blocks and implement high-speed training on data center AI systems (e.g., Cerebras CS-1 ML accelerator and Argonne's Aurora exascale supercomputer), end-to-end model training workflows and explore AI accelerators for simulation applications.
Position Requirements:
Recent PhD in a computer science, physical sciences or engineering or related field.
Comprehensive experience programming in one or more programming languages, such as C, C++, and Python.
Experience with machine learning methods and deep learning frameworks, including tensorflow, pytorch.
Software development practices and techniques for computational and data-intensive science problems.
Experience with X-ray science techniques (e.g., crystallography).
Experience and skills in interdisciplinary research involving computer and material scientists.
Experience on applied machine learning (e.g., successful projects that used ML to resolve scientific problems).
Experience with high-performance computing and/or scientific workflow.
Ability to provide project leadership.
Exceptional communication skills, ability to communicate effectively with internal and external collaborators and ability to work in team environment
Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
This position will develop artificial intelligence (AI) and machine learning (ML) for DOE Scientific User Facilities. In particular, the project aims to develop AI/ML models that have been trained to detect specific features in X-ray detector data at the Advanced Photon Source (APS) at Argonne and Linac Coherent Light Source (LCLS) at SLAC. Because such models can run at high speeds, thanks to advances in AI streaming inference accelerators, it becomes feasible to extract salient information from in-flight data, in real time, and thus 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 physics-based AI/ML models, developing workflow building blocks and implement high-speed training on data center AI systems (e.g., Cerebras CS-1 ML accelerator and Argonne's Aurora exascale supercomputer), end-to-end model training workflows and explore AI accelerators for simulation applications.
Position Requirements:
Recent PhD in a computer science, physical sciences or engineering or related field.
Comprehensive experience programming in one or more programming languages, such as C, C++, and Python.
Experience with machine learning methods and deep learning frameworks, including tensorflow, pytorch.
Software development practices and techniques for computational and data-intensive science problems.
Experience with X-ray science techniques (e.g., crystallography).
Experience and skills in interdisciplinary research involving computer and material scientists.
Experience on applied machine learning (e.g., successful projects that used ML to resolve scientific problems).
Experience with high-performance computing and/or scientific workflow.
Ability to provide project leadership.
Exceptional communication skills, ability to communicate effectively with internal and external collaborators and ability to work in team environment
Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
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