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Field
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Engineering, Aerospace Engineering, or a related field. Degree must be conferred upon hire. Preferred Qualifications Applied expertise in optimal control, heuristic optimization, graph search algorithms, and
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algorithms, and machine learning to solve complex aerospace engineering challenges. Developed and implemented AI-driven solutions for autonomous lunar and asteroid landings, as well as cislunar operations
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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the analysis of multimodal spatial omics data across multiple projects, collaborating closely with experimental biologists. Main tasks and responsibilities: Develop and implement robust computational pipelines
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research outputs. The candidate will need to demonstrate agility in managing and leading multiple projects and work independently in this ever-changing and fast-paced sector. The position is funded by CAR, a
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, Prof Anton van den Hengel and his research team to publish research outputs. The candidate will need to demonstrate agility in managing and leading multiple projects and work independently in this ever
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| Mechanical and Aerospace Engineering Perform basic research in computational fluid dynamics, including problem setup, simulation and advanced post-processing for multiple projects. Emphasis to be placed
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develop signal processing algorithms to characterize structural health in microreactors and other advanced nuclear reactor technologies. Metrics for success will include scientific output, disseminating
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responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling, or generative modeling. Collaborating with
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with Neutrons. Jason Fry’s group has worked on significant contributions to Nab and BL3 and supports their experimental efforts through work from the PI and undergraduate students through multiple NSF