Research at the Institute of Mathematics within the joint DFG project "Numerically Efficient Learning of Generative Models and Beyond" in the research groups of Prof. Dr. Benjamin Gess and Prof. Dr. Gabriele Steidl. No teaching duties.

The project addresses mathematical questions in the field of artificial intelligence. Responsibilities include in particular:

• Research on the mathematical foundations, stability, efficiency, and numerical realization of generative models
• Research in one or more of the following areas:
  - Efficient diffusion models and geometry-informed modeling
  - Transformer models
  - Scientific Machine Learning
  - Nesterov and Anderson acceleration as well as Newton methods in machine learning

The research will be conducted in cooperation with Tsinghua University, Beijing (Ch. Bao), and Wuhan University (Y. Jiao).

• Successfully completed university degree (Master, Diplom or equivalent) and a completed PhD by the time of appointment in mathematics or a closely related field
• excellent knowledge of the mathematics of machine learning, especially generative models; demonstrated by publication in leading journals of the field
• excellent programming skills, particularly in Python
• excellent written and spoken English
• knowledge of stochastic analysis desirable
• knowledge of optimization methods and their mathematical analysis desirable
• experience with numerical methods in machine learning desirable

The subject-specific knowledge can be demonstrated in particular by relevant coursework, theses, research projects and/or publications. Relevant scientific publications are required.

For further information about the position, please contact Anastasiia Olshevska (olshevska@math.tu-berlin.de).

Please send your application with the reference number and the usual documents only by email (bundled in one PDF document, max. 5 MB) to the secretary's office, attn. Ms. Anastasiia Olshevska, via olshevska@math.tu-berlin.de.
Please include in particular a short letter of motivation, a complete and current CV in table form, certificates and transcripts (Bachelor's and Master's), where applicable a list of publications, and proof of completion of the PhD. In addition, names and contact details of up to two referees may be provided; please ensure that these persons consent to the disclosure of their contact details and to being contacted. Reference letters may be sent directly to the above email address by the application deadline.

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. Applications from people of all nationalities and with a migration background are very welcome.