Professor

Gitta Kutyniok

Professor

Ludwig-Maximilians-Universität München

Germany

Research Interests

Computational Neuroscience

50%

Statistics

20%

Image Reconstruction

30%

Network Analysis

20%

Deep Learning

20%

Propagation Modeling

20%

Mathematics

20%

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Recent Grants

Grant: Close

A Data-Based Framework for Mathematical Foundations of Deep Neural Networks

Open Date: 2023-01-01

Close Date: 2024-01-01

Grant: Close

Research Focus “Physics and Security” at the Center for Advanced Studies (CAS)

Open Date: 2022-01-01

Close Date: 2024-01-01

Grant: Close

ONE Munich Strategy Forum Project “Next generation Human-Centered Robotics: Human embodiment and system agency in trustworthy AI for the Future of Health”

Open Date: 2022-01-01

Close Date: 2025-01-01

Grant: Open

Konrad Zuse School of Excellence in Reliable AI (relAI)

Open Date: 2022-01-01

Close Date: 2027-01-01

Grant: Close

6-Month Programme “Mathematics of Deep Learning”

Open Date: 2021-07-01

Close Date: 2021-12-01

Positions(2)

Publisher
source

Ludwig-Maximilians-Universität München

Germany

PhD Positions in Reliable Artificial Intelligence at Ludwig-Maximilians-Universität München and Technical University of Munich

The relAI Konrad Zuse School of Excellence in Reliable AI is now accepting applications for its 2025 PhD program, jointly organized by Ludwig-Maximilians-Universität München (LMU) and Technical University of Munich (TUM), and funded by the DAAD German Academic Exchange Service. This interdisciplinary program focuses on reliable artificial intelligence, offering research opportunities at the intersection of machine learning, mathematics, engineering, and the responsible development of AI systems. Research areas include AI safety, security, privacy, responsibility, algorithmic decision-making, robotics, healthcare, and mathematical foundations. PhD candidates benefit from a vibrant academic environment, professional development, and close collaboration with leading researchers at both LMU and TUM. Each doctoral researcher is supervised by a relAI Fellow and enrolls at either LMU or TUM, depending on the supervisor's affiliation. Notable relAI Fellows and spokespersons include Prof. Gitta Kutyniok (LMU Director of relAI School), Prof. Frauke Kreuter, Prof. Eyke Hüllermeier, Prof. David Rügamer, Prof. Albrecht Schmidt, Prof. Hinrich Schuetze, Prof. Volker Tresp, Prof. Solveig Vieluf, and Prof. Stephan Günnemann. Funded positions offer a full three-year salary (TV-L E13) and additional support such as travel grants for conferences or research stays. Applicants must hold an excellent master's degree (or equivalent) in computer science, mathematics, engineering, natural sciences, or other data science/machine learning/AI-related disciplines. A strong interest in reliable AI, including safety, security, privacy, or responsibility, is required. Proof of English proficiency at level C1 or equivalent (or proof that the degree was taught mainly in English) is mandatory. Application materials include a CV, diplomas, transcripts, a short motivation statement, choice of two preferred relAI Fellows as supervisors with explanations, names and addresses of two referees for recommendation letters, and proof of English proficiency. The application deadline is January 13, 2026. For more information and to apply, visit the official relAI application portal.

just-published

Publisher
source

Gitta Kutyniok

Ludwig-Maximilians-Universität München

.

Germany

Artificial Intelligence

A fully funded PhD position is available in the Mathematical Foundations of Artificial Intelligence at Ludwig-Maximilians-Universität München, under the supervision of Professor Gitta Kutyniok. The successful candidate will join the vibrant Munich AI Ecosystem and the Munich Center for Machine Learning, engaging in cutting-edge research topics such as explainability, generalization, graph neural networks, inverse problems and AI, next generation AI computing, PDEs and AI, learning physical laws by AI, reliable AI, spiking neural networks, and sustainable (energy-efficient) AI. Applicants should have an excellent degree in mathematics, computer science, or a related field, and demonstrate strong analytical, programming, teamwork, and communication skills, as well as a high level of commitment and proficiency in English. The position offers a competitive salary (up to TV-L E13), flexible working hours, individual career support, and opportunities for professional development, including collaboration with leading researchers and participation in international conferences and training programs. The contract is for three years, with possible extension. Interested candidates should apply via the provided link before October 22, 2025. For more information about the research group and activities, visit the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence website.

1 month ago

Collaborators(3)

Michael Sailer

Professor (W3) for Learning Analytics and Educational Data Mining

University of Augsburg

GERMANY
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Philipp Petersen

Universität Wien

AUSTRIA
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Gjergji Kasneci

-

GERMANY
View Details