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the Manchester BHF CRE: Geometric Deep Learning for Complex Manifolds: Novel deep learning theories, models and architectures to simulate interactions within non-Euclidean, patient-specific cardiovascular
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-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden
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foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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: Software Development Develop, optimize, and maintain software for AI research projects. Collaborate with researchers to implement state-of-the-art machine learning models. Work with large-scale datasets
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. Research in the lab is highly multidisciplinary and quantitative, requiring development and use of cutting edge computational modeling and statistical analyses (including machine learning and artificial
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areas: Developing and training robust machine learning surrogates to replace computationally expensive high-fidelity simulations, enabling exploration of vast design spaces. Formulating optimization
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- Provisional Positions Department's Website: https://cosmos.ualr.edu/ Summary of Job Duties: The Graduate Research Assistant will develop machine learning and artificial intelligence (ML/AI)-driven socio
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xenograft and cell line models, and analyze clinical breast tissue samples. Additional duties include lab maintenance and organization. Work will include delivery of medicines, marking responses and
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: familiarization with the workflow platform and machine learning concepts; development of web interfaces for data silo registration and federated training sessions monitoring; implementation of back-end components
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Summary We are seeking an undergraduate student to assist with data preprocessing and machine learning tasks. Career Readiness Competencies: Communication Professionalism Teamwork Essential