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Field
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of pediatric cancer patients and discover the impact of therapeutic exposure on development of secondary neoplasms in adult survivors. Position Responsibilities: Evaluate bulk-tumor deconvolution algorithms
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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on the littoral environment. Algorithm development includes photogrammetric measurements of wave parameters, image stabilization, and use of AI/ML models for image segmentation and classification. Algorithms will
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accurate completion. Algorithm Design: Design and implement algorithms for tensor completion, considering the unique challenges posed by sparse and multidimensional network data. This involves developing
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Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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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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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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problem-solving skills, with a focus on developing innovative solutions for multi-modal LLMs. Self-motivated and able to work independently, managing multiple tasks and projects in a fast-paced environment
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy