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Field
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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on circular economy research Experience in working in the genetic algorithm and artificial neural networks is preferred. Experience in manufacturing process modeling of advanced manufacturing technologies
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real-world applications in green chemistry and industrial synthesis. Key Responsibilities: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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, in collaboration with researchers in the computational field; d) Contribution to the development, validation, and optimization of predictive models (Random Forest, Gradient Boosting, Neural Networks
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physiology and neural activity in the brain, gastrointestinal (GI) tract, and other peripheral organs. These projects have a high potential for translation towards treating a variety of neurologic and
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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for action ("affordances") shape neural representations, perception, and behavior. Why this position? You will sit at the center of a uniquely cross‑disciplinary team and work closely a network of
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of neuronal loss to better understand why neurons die or axons are damaged to ultimately establish new strategies for the preservation or restoration of neural tissue. We use multiple approaches, but focus