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Field
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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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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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, training, and evaluating neural network models Experience applying machine learning to large geospatial, Earth observation, or environmental datasets Ability to work independently and take responsibility
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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
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communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL - Quantum Reinforcement
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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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classes and their roles in scientific applications, such as deep neural networks (DNNs), convolutional neural networks (CNNs), transformer models, and graph-based neural networks. Familiarity with software
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neural basis of learning and memory across the adult lifespan in diverse samples of females and males. We are interested in understanding why some adults maintain a high level of memory function into late
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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performance and/or neural structure/function that govern the rate of cognitive development in childhood and/or old age. The post holder will have an interest in theory-based developmental cognitive (neuro