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Field
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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artificial intelligence and neural networks, with a particular focus on applying these numerical approaches to quantum many-body systems, such as correlated 2D materials, quantum Moire systems, frustrated
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that methodological advances are developed with direct translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model-graph neural network architectures for gene
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs) Contribute to the strategic direction of research
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learning/statistical learning (e.g., regression/ML pipelines, neural networks, model evaluation), and experience using common ML libraries. Ability to work independently and communicate effectively in
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover
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Mellon University is looking for a talented postdoctoral associate to interrogate the circuit dynamics underlying motivated behavior. We are looking to explore the electrophysiological properties of neural