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Field
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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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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
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio-)Statistics
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-driven algorithms (e.g., neural networks, reinforcement learning) for the creation of surrogate models and the autonomous optimization of high-dimensional design spaces. Experience simulating hybrid
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). AI methods, and especially machine learning (ML) with deep neural networks have replaced traditional data analysis methods in recent years. The Technical University of Munich (TUM), together