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multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address fundamental scientific challenges
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demonstrated ability to work within a geographically distributed networks of collaboration Proven experience in developing and implementing machine learning models and algorithms, ideally in the healthcare
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resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's computational resources, including the Frontier supercomputer
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demonstrated ability to work within a geographically distributed networks of collaboration Proven experience in developing and implementing machine learning models and algorithms, ideally in the healthcare
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terms of research and education, covering all aspects of computer science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
and generative models. Develop novel algorithms for generative modeling tasks and optimize LLM/GPT-like models on large datasets. Stay abreast of advancements in language modeling and generative AI
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modeling techniques and artificial intelligence methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data management and
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opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach