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Field
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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complex dynamic environments. Robot platform and sensor system hardware and software integration. Algorithms and systems verification and validation. Report writing and presentations. Job Requirements
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verification, and attention to detail; ability to evaluate the limitations, risks, costs, and research validity of AI-assisted methods. PhD in a related area and/or equivalent experience/training. Salary
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) is a generalization of the standard QBF that allows them to succinctly encode many hard problems arising in hardware verification, circuit design and software synthesis. The main goal of this project
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-throughput phenotyping at ORNL's Advanced Plant Phenotyping Laboratory (APPL) with paired geochemical and microbial measurements. Utilize laboratory equipment and software that may include a Picarro gas
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modification of embedded flight control software in embedded C/C++ using ROS/ROS2 or similar platforms Verification, validation and testing of flight software (e.g., command and control module, PID and LQR
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software design patterns that are applicable to a wide variety of HPC applications and ensure sustainability of research software. Follow team planning, documentation, verification and validation, and
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porous-silicon experience is advantageous but is not essential where a candidate brings strong adjacent expertise at the engineering-biology interface. Essential criteria A PhD awarded, or thesis submitted
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for specifying and reasoning rigorously about the behaviour of software and AI systems, drawing on areas such as programming language theory, logic and category theory. Where appropriate, you will also design
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excellence. Job Description This position is for Centre for Research on Intelligent Software Engineering (RISE). Design methods for secure and private data collection and sharing for large model training