11 coding-"https:"-"https:"-"https:"-"CONSERVATOIRE-NAT" Postdoctoral positions at Oak Ridge National Laboratory
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coding (Python) for building energy modeling and controls Preferred Qualifications: Expertise in modern optimal control techniques (e.g., AI based controls) High level of competence in coding and scripting
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those skills to a variety of problems, and the ability to determine and understand the broader context of his or her research. Preferred Qualifications: Proficiency in multiple modern coding languages is
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standard (version control, unit testing, continuous integration, etc.). Experience in the development of large-scale physics simulation codes, including coupling of multiple codes, and an understanding
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
familiarity with AI/ML algorithms, for generative materials design, or for knowledge extraction, e.g. causal ML or symbolic regression, etc. Strong demonstrated background in coding for data analysis using
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and benchmark PyORBIT code. Participate in scientific conferences, workshops, meetings and publishes results in the form of SNS technical notes and memos, workshop and conference proceedings, and
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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing (HPC
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simulation codes, including computational scaling and efficiency, for hybrid exascale supercomputing systems. Programming model for multicore and heterogeneous architectures such as graphical processing units
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and modify open-source codes to develop technical solutions and explore integrated design and optimization of tritium breeding blanket systems and related component designs. The successful candidate
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Scientist you will be responsible for: Developing high-quality code following best practices in the community for documentation, provenance, version control, etc. Participating in research projects in AI
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., code interpreters, simulation frameworks, databases, lab instruments) and evaluation for long-horizon tasks. Experience with RL and post-training (reward modeling, preference learning, offline/online RL