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of application development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). A record of productive and creative research as proven by
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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cluster. The successful candidate will conduct research under multiple Eratosthenes Centre of Excellence projects funded by the European Union, including the flagship Excelsior H2020 Teaming project (https
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technology and develop human resources. The AI Computing Team explores the design and realization method of advanced machine learning systems by working across multiple layers, including circuits, devices
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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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