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                Field
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                computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and 
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                lab/field validation. - Strong DSP/algorithms and real-time control; proficiency in Python/Matlab. - Experience integrating optics with electronics and firmware. - Track record of publications in fiber 
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                Center’s mission is to create the theoretical, computational and algorithmic paradigms that will enable us to understand the programs of life and how they connect across scales, from proteins to cells 
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                outcomes. The individual will be expected to develop stimulation strategies and testing algorithms, write code, and develop software. They will do extensive validation and testing, under the supervision 
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                optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise 
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                algorithmic fairness Formulation of new problems and research directions and translating topical issues into algorithmic problems Designing new algorithms and investigating their performance on synthetic and 
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                The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago, and medicine. Key Responsibilities Collaborate with researchers to design, develop, and refine large language and generative models. Develop novel algorithms for generative modeling tasks and optimize 
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                to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems 
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                on classical supercomputers. Design and optimize classical simulation algorithms for quantum circuits. Explore approximate classical simulation algorithms (e.g., tensor networks, circuit cutting) to improve 
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                algorithmic fairness Formulation of new problems and research directions and translating topical issues into algorithmic problems Designing new algorithms and investigating their performance on synthetic and