PhD or Post-Doc Candidate (m/w/d)

Arbeitgeber: Ludwig-Maximilians-Universität München

Die Ludwig-Maximilians-Universität München ist eine der führenden Universitäten in Europa mit einer über 500-jährigen Tradition. Sie steht für anspruchsvolle akademische Ausbildung und herausragende Forschung.

Einrichtung Fakultät für Mathematik, Informatik und Statistik - Mathematisches Institut Vergütung TV-L Umfang Vollzeit (100%) Besetzungsdatum 01.02.2027 (limites until 30.04.2028)

## About us:

The Chair for Mathematical Foundations of Artificial Intelligence (MATH4AI, [https://www.math.lmu.de/math4ai](https://www.math.lmu.de/math4ai)), led by Prof. Dr. Gitta Kutyniok at the Mathematical Institute of Ludwig-Maximilians-Universität München (LMU) and established under the Hightech Agenda Bayern, develops the mathematics for the next generation of AI. We are an international team of around 35 researchers from 14 countries, organized in six research clusters at the interface of mathematics and artificial intelligence.

Our research develops the mathematical foundations of AI — representation power, learning, generalization, and explainability — and turns them into reliable, sustainable, and trustworthy AI methods. Current directions range from trustworthy and explainable AI to next-generation, brain-inspired and energy-efficient AI computing, including spiking neural networks and the fundamental limits of AI. Beyond its own research, the chair leads and shapes major initiatives within the strong Munich AI ecosystem and beyond, including the AI-HUB@LMU, the Konrad Zuse School of Excellence in Reliable AI (relAI), and the Bavaria–Saxony initiative gAIn for next-generation (green) AI computing.

**The Project:**
The advertised position is part of the project "Spiking Neural Networks: Theoretical Foundations, Trustworthiness, and Energy-Efficiency", carried out in cooperation with Prof. Dr. Holger Boche (Chair of Theoretical Information Technology, Technical University of Munich). Spiking neural networks model the human brain more closely than classical neural networks and are a promising route to addressing two central challenges of current AI: its enormous energy demand and its limited trustworthiness. The project aims to develop the mathematical foundations of spiking neural networks, with particular emphasis on reliability and energy efficiency. It is part of the DFG Priority Programme SPP 2298 "Theoretical Foundations of Deep Learning".

Wir suchen Sie:

# PhD or Post-Doc Candidate (m/w/d)

am Standort München

## Das sind Ihre Aufgaben:

- Conduct research on the questions of the project "Spiking Neural Networks: Theoretical Foundations, Trustworthiness, and Energy-Efficiency"
- Publish research results in leading national and international journals
- Present research results at international conferences
- Contribute to the activities of the DFG Priority Programme SPP 2298

## Das sind Sie:

- An excellent university degree (Master or, for postdoctoral candidates, a doctorate) in mathematics, computer science, or a related field, ideally with a focus on the mathematical foundations of artificial intelligence and/or image processing and inverse problems
- Solid programming skills
- Business-fluent communication in English, both written and spoken (German is welcome but not required)

## Das ist unser Angebot:

- A highly topical and impactful field of research
- Unique opportunities to network with the German interdisciplinary research community on the theoretical foundations of deep learning through the activities of SPP 2298
- An inspiring, collegial atmosphere in a highly international research group
- For PhD candidates, the opportunity to pursue a doctorate
- A workplace in a central and attractive location in Munich, easily accessible by public transport

Remuneration is based on qualifications and professional experience up to TV-L E13.

Also possible in a part-time capacity.

Schwerbehinderte Personen werden bei im Wesentlichen gleicher Qualifikation bevorzugt.

## Kontakt:

Please send your application as a single PDF (max. 5 MB), including the usual documents (cover letter, CV, copies of certificates, list of publications, and contact details for two references), by **November 20th 2026,** to Prof. Dr. Gitta Kutyniok at . For questions, please contact Prof. Dr. Gitta Kutyniok ().

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