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Field
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uncertainty quantification using Bayesian inference techniques based on experimental and/or synthetic data. Investigating efficient data representations (embeddings) for raw data recordings from the in vitro
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-machine interfaces. Experience with AI methods such as generative models, reinforcement learning, online/adaptive learning, or uncertainty quantification. Research experience in rehabilitation engineering
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output properties. These approaches often ignore material characterization information and/or proper information-rich AI surrogate models (thereby having no capability for uncertainty quantification (UQ
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reuse across biological modalities. Develop end-to-end ML workflows (feature/representation learning, training, validation, benchmarking, and uncertainty quantification) for multi-omics and related data
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Systems Design, Analysis and Testing, (3) Laser Diagnostics and Sensors in Hypersonic and Extreme Environments, (4) Mathematical Foundations of AI, Uncertainty Quantification, and Reduced Order Modeling
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trustworthiness: developing and validating trustworthy AI methods for space applications, with emphasis on explainability, robustness, uncertainty quantification, safety assurance and compliance with relevant