Kopie von Topics relAI PhD call - Form

Research Topics

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Coordinators of Zuse School of Excellence in Reliable Artificial Intelligence

Munich Data Science Institute

Technical University Munich

Walther-von-Dyck-Straße 10

85748 Garching, Germany

coordinators@zuseschoolrelai.de

Coordinators of Zuse School of Excellence in Reliable Artificial Intelligence

Munich Data Science Institute

Technische Universität München

Walther-von-Dyck-Straße 10
85748 Garching, Germany

coordinators@zuseschoolrelai.de

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Please select topics of the relAI Research Areas fitting to your research
Mathematical & Algorithmic Foundations	Statistical aspects of reliable AI (including causality, uncertainty etc.) Mathematical foundations of Safe and Secure AI (including robustness, generalisation bounds, privacy, formal verification, etc.) Uncertainty quantification (representation and quantification of uncertainty in AI and ML) Foundations of trustworthy AI (including explainability, fairness etc) Causal and machine learning (e.g., causal ML) Bayesian methods (for uncertainty, decision-making, interpretability, etc.) Generative AI and reliability Provably safe machine learning Resource-efficient machine learning Design of unbiased foundation models (as opposed to posthoc alignment)
Medicine & Healthcare	Reliable ML for medical imaging and sensing Multi-modal ML / AI Causal ML for interventions and treatment planning Explainable ML for drug discovery Privacy-preserving AI Trustworthy medical foundation models Confidential computing for AI Systems Safe and responsible clinical AI: evaluation of AI under clinical aspects Safe and responsible clinical AI: fairness and bias of AI in medicine Safe and responsible clinical AI: computer assisted diagnosis and outcome prediction
Robotics & Interacting Systems	Safe AI-supported manipulation and locomotion Safe autonomous vehicles (cars, drones, underwater vehicles) Safe planning and control under uncertainty Reliable foundation models for robotics Robust learning-based control with guarantees Secure AI cloud computing for embodied agents Privacy-preserving AI for human - robot interaction Reliable physics-informed learning for robotics Trustworthy AI Systems for cloud, edge, and mobile computing
Algorithmic Decision-Making	(Automated) decision making of societal decisions (focus on reliable indicators that are understandable and communicable to decision makers and those affected by decisions) Data quality (biases/fairness/efficient selection to save computational resources) Value alignment (models reliably reflecting societal values and aligned pluralistically) Societal indicators (using AI to enhance trustworthy quantitative SDG indicators) Causal decision-making (e.g., off-policy learning, off-policy evaluation, reinforcement learning) Preference learning (combining machine learning and preference modeling) Alignment of foundation models Mechanistic interpretability for explainable decisions Algorithmic decision-making in the public sector (ie, high-stakes, dynamic environments)
Others	AI in Education
Please select relAI Central Themes fitting to your research
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Kopie von Topics relAI PhD call - Form

Research Topics

Please select topics of the relAI Research Areas fitting to your research

Please select relAI Central Themes fitting to your research

Coordinators of Zuse School of Excellence in Reliable Artificial Intelligence

Munich Data Science Institute