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codes and making use of high-performance computing (HPC) tools. Develop accurate, computationally efficient physics-based and data-driven CFD models which the candidate will use to simulate the chain of
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numerical simulation workflows. Desirable Criteria: Experience working in Linux and high-performance computing environments. Experience with turbulence modelling (RANS, LES, or hybrid RANS-LES methods
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activities in areas such as numerical analysis, mathematical modelling, development and analysis of algorithms, machine learning, optimization, scientific software development and high-performance computing
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environments, and high-performance or parallel computing is desirable. The candidate will also prepare technical documentation, reports, peer-reviewed publications, and presentations and collaborate
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workflows for grid modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The
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into TANGO, our platform for techno-economic analysis of a wide range of geoenergy systems, and capable of standalone simulation. Functioning as the engineering counterpart to our geothermal reservoir
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· Experience maintaining highly active code bases using versioning software (e.g., Git). · Experience working on high-performance computing (HPC) systems and optimizing codes for various
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research in engineering biology. This is a broad, hands-on role at the interface of Linux systems, high-performance computing, cloud infrastructure, Kubernetes, Slurm, storage, monitoring, and
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-based surrogate models. Experience with high-performance and parallel computing. Familiarity with digital twins and real-time deployment of predictive models is an advantage. Who we are The two
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Two postdoc positions in Thermofluid Design and Digital Modeling of Cooling Systems for Electroch...
process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design is both experimentally realizable and