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Field
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the
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algorithm development in conjunction with extensive applications in the fields of nanoscience and energy-related materials. Position Requirements a PhD in physics, or closely related field. Degree must have
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properties, or multiscale modeling will be preferred. The appointee will be expected to (i) develop and implement machine learning algorithms for materials design and discovery; (ii) collaborate with
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aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimising PIC algorithms for modern
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 16 days ago
Experience with implementation of photometric and time-series detrending algorithms and high-performance computing Experience with large software projects, including proficiency across the software development
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
Experience with implementation of photometric and time-series detrending algorithms and high-performance computing Experience with large software projects, including proficiency across the software development
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be to develop high fidelity simulations and/or algorithms to enable Bragg coherent diDraction imaging. We expect x-ray ptychography and coded aperture methods to play a fundamental role in creating a
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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advanced automated machine startup and commissioning tools. What You Will Do: Conduct pioneering research in the development and application of machine learning algorithms for accelerator physics
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a multidisciplinary research team focused on developing energy-efficient and fault-tolerant AI systems that can operate reliably in the radiation-rich environment of space. The project integrates