TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic
research institutions in the country. Founded in 1828, today it is a globally oriented, regionally anchored top
university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world's
most pressing issues. In research and academic programs, the university unites the natural and engineering sciences
with the humanities, social sciences and medicine. This wide range of disciplines is a special feature, facilitating
interdisciplinarity and transfer of science to society. As a modern employer, it offers attractive working conditions to
all employees in teaching, research, technology and administration. The goal is to promote and develop their
individual abilities while empowering everyone to reach their full potential. TUD embodies a university culture that is
characterized by cosmopolitanism, mutual appreciation, thriving innovation and active participation. For TUD
diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we welcome all
applicants who would like to commit themselves, their achievements and productivity to the success of the whole
institution.
At the Faculty of Electrical and Computer Engineering, Institute of Communication Technology, the
Deutsche Telekom Chair of Communication Networks offers a position as
Research Associate (m/f/x)
(subject to personal qualification, employees are remunerated according to salary group E 13 TV-L)
starting as soon as possible. The position is limited until June 30, 2027. The period of employment is
governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG).
Tasks: The position involves research on energy-efficient agentic AI for computer and communication
networks. The candidate will contribute to the design, implementation, and evaluation of real testbeds
and emulators to validate the developed concepts. The research will address novel methods for defining
agent skills, applying Retrieval-Augmented Generation (RAG), and integrating agentic AI with
communication and computing network infrastructures. A central objective is to reduce the energy
footprint through efficient model design, inference optimization, and resource-aware deployment
strategies. The work will further investigate novel computing platforms, including spiking neural
networks, neuromorphic architectures, and analog computing, as potential substrates for energy-
efficient AI inference and resource-aware components of low-power agentic AI systems. The philosophy
of our team is "Research that matters." Therefore, you are expected not only to work on theory but also
to program and deploy your own research in our testbeds using state-of-the-art software libraries and
the results of cooperative research with other team members. Your work will be beneficial not only to our
academic partners but also to many industry members involved in projects with our team. The position
also includes supervising student work related to the research topics. The work results will be published
at international conferences and in recognized journals.
Requirements:
- university degree (Diploma/Master) in Electrical Engineering, Telecommunications, Information
Systems, Computer Science, or similar.
- programming skills in C++, Python, or Golang
- Knowledge in agentic AI concepts, agent skill definition, Retrieval-Augmented Generation (RAG),
communication networks, and computational network concepts is a plus.
We offer:
- the opportunity for engaging and independent work within a flat hierarchy, in an open-minded
team and supportive atmosphere
- flexible working hours
- 30 days of vacation per year (based on a 5-day workweek)
- extensive opportunities for professional development and continuing education
- health care and sports programs offered by TUD
- a discounted job ticket (also available as a Deutschlandticket)
- participation in the supplementary pension scheme for employees in the public sector via VBL
(Federal and State Government Employees Retirement Fund)
