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Field
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-guided) Evolutionary trajectory analysis and fitness landscape modeling Integration of predictive algorithms with experimental iteration cycles High-throughput screening and selection platform development
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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electron beams, advanced beam-manipulation for precise electron-beam shaping, and ML for accelerator science. Responsibilities Develop and deploy ML algorithms for autonomous operations and optimization
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disease progression. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will
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testing of model-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs
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giving academic presentations. *Trained as a theorist in either condensed matter (CM), atomic, molecular and optical (AMO) physics or in quantum information theory. *Interested in quantum algorithm and
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analysis across time and conditions Algorithm design and modeling. The role offers significant intellectual freedom and opportunities to shape the direction of the research. Minimum Qualifications: • PhD in
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and resilience across heterogeneous computational resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's
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grid planning. Design and code efficient algorithms for large-scale optimization problems using the Julia programming language and packages such as JuMP.jl. Experience with Xpress and Gurobi are a plus
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, Optimization, and AI • ML/AI for mobility prediction and optimization • Graph algorithms, network science • Spatiotemporal modeling • Operational research for mobility and infrastructure • Real-World Practice