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Postdoctoral Research Associate : Scalable Machine Learning for Coupled Physics | Research Associa1

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Postdoctoral Research Associate : Scalable Machine Learning for Coupled Physics

Location:
Oak Ridge, TN
Description:

Requisition Id11587 Overview: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an extraordinary 80:year history of solving the nation's biggest problems. We have a dedicated and creative staff of over 6,000 people Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL's broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation. The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi:scale coupled physics simulations in support of the missions and programs of the US Department of Energy (DOE). ORNL's CCP conducts world:class research and development in multi:scale computational coupled physics, large scale data analytics and DL, and model:data integration at the DOE's leadership class Computing Facilities (LCFs). The successful candidate will demonstrate strong expertise and skills in data analytics, development of surrogate and generative DL models, high:performance computing (HPC), and computational sciences. Major Duties/Responsibilities: : Participate in: (1) design and implementation of scalable DL algorithms, (2) design and architecture of integrated, multi:scale, coupled:physics computer codes, and (3) documentation, verification and validation, and software quality activities.: Author peer reviewed papers for journals and conferences, technical reports, open:source software, and represent the organization by making technical presentations at workshops and conferences.: Collaborate within a multi:disciplinary research environment consisting of computational scientists, computer scientists, experimentalists, engineers, and physicists conducting basic and applied AI/DL research in support of the Laboratory's missions.: Engage with the broader DL community to develop and apply scalable physics:informed DL techniques to application areas of interest to the CCP group.:Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace : in how we treat one another, work together, and measure success. Basic Qualifications: : A PhD in applied mathematics, computer science, or an AI related field completed within the last 5 years: Demonstrated expertise in writing advanced software in Python: Demonstrated experience with the LINUX operating system, LaTeX, Git, Python: Demonstrated expertise in the design and implementation of deep learning algorithms in PyTorch: Expertise in object:oriented programming, and scripting languages: Parallel algorithm and software development using the message:passing interface (MPI), particularly as applied to AI/ML algorithms : Demonstrated effective written and oral communication skills, a proven publication record, and effective interpersonal skills. Preferred Qualifications: : Experience working in a multi:disciplinary research environment that follows modern software quality standards (version control, unit testing, continuous integration, etc.).: Experience in the development of large:scale physics simulation codes, including computational scaling and efficiency, for hybrid exascale supercomputing systems.: Programming model for multicore and heterogeneous architectures such as graphical process
Posted:
May 7 on Tip Top Job
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