20 coding "https:" "https:" "https:" "Lund University" research jobs at Oak Ridge National Laboratory
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(https://www.olcf.ornl.gov/frontier ) and plant phenotyping (https://www.ornl.gov/appl ). GPTgp is a pilot project initiated in September 2025 with funding from the US Department of Energy and will
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Computational/theoretical chemistry and/or physics, chemical engineering, materials or a closely related field completed within the last 5 years. Preferred Qualifications: Experience with coding, electronic
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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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scientific outputs that may include peer-reviewed publications in top-tier water journals, professional scientific code/software contributions, and high-quality datasets. Candidates must also be willing
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subassemblies. The successful candidate will drive the development of high-fidelity dynamic simulation tools to model complex rotating systems and will apply advanced rotor analysis and design codes to predict
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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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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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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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., 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