20 algorithm-development research jobs at Oak Ridge National Laboratory in Postdoctoral
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Requisition Id 16985 Overview: We are seeking a Postdoctoral Research Associate to join a large research team working on the development and implementation of novel algorithms for scientific
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materials science applications and algorithms for OLCF supercomputers Develop and apply advanced, compute-intensive materials simulation methodologies to study the fundamental properties of molten salt
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thermal systems, controls, or AI/ML applications Experience developing and deploying reinforcement learning algorithms for real-time control applications Experience with hardware-in-the-loop (HIL) testing
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, reactor core, spent fuel, and fuel cycle applications through leadership in the development and analysis of SCALE and/or the Virtual Environment for Reactor Applications (VERA) simulations and in
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-preservation algorithms. The successful candidate will develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work
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and porting of scalable science applications and algorithms for OLCF and other leadership computers. Debugging and tuning applications for high performance. Developing performance models of existing
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, algorithms, applications, compilers, runtimes, and system-level constraints. Work with project teams to formulate and evaluate novel research ideas, develop research software and prototypes, mentor students
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expected to bring expertise in all of them: Experience developing or implementing quantum algorithms for Hamiltonian simulation. Background in quantum materials or quantum many-body physics, particularly
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hardware. As part of our team, you will perform research to develop new scalable quantum simulation algorithms, based on multi-linear representation theory, and apply them to real world applications spanning
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navigation, trajectory planning, and point cloud mapping; and (3) deploy the developed algorithms on a physical mobile robot operating in GPS-denied, low-light, and geometrically repetitive environments