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. TUD has established the Research Training Group "AirMetro - Technological & Operational Integration of Highly Automated Air Transport in Urban Areas" (RTG 2947), funded by the German Research Foundation (DFG). This interdisciplinary group, involving five faculties and the German Aerospace Centre (DLR), will conduct research on 11 research topics. The goal is to address the technical and social challenges of Innovative Air Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring international collaboration with mentors from the USA, Asia, and Europe. TUD and the RTG embody 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.

The Research Training Group RTG 2947 "AirMetro", funded by the DFG, offers a position, as

Research Associate / PhD Student (m/f/x)
(subject to personal qualification employees are remunerated according to salary group E 13 TV-L)

starting May 1, 2026. The position is limited until April 30, 2029. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG). The position aims at obtaining further academic qualification (usually PhD).

Job ID: RTG2947-T2/2
Title: Surrogate metamaterial modelling for effective vehicle communication in IAM operations
Supervisor: Prof. Michael Kaliske, Chair of Structural Analysis and co-supervised by at least one additional professor plus an international tutor of the RTG

Description of the PhD topic:
This PhD topic focuses on far-field communication effects of building-integrated electromagnetic meta-surfaces, such as façades or dedicated urban structures, with the aim of controlling and improving radio propagation for IAM operations. While Cohort 1 concentrates on high-fidelity finite-element modeling of the electromagnetic near field of meta-surfaces under coupled physical effects, this project builds on those results to model far-field behavior relevant for communication networks. The objective is to develop reduced-order surrogate models using physics-informed machine learning (e.g. PINNs, PIGNNs) that predict network-level quality metrics such as RSSI and SINR, given the geometry, placement, and orientation of base stations and meta-surfaces in an urban environment.
The surrogate models provide fast estimates of far-field radio coverage in urban environments that can be used during planning and operation. This makes it possible to assess communication quality along flight trajectories and to study how the placement and orientation of base stations and meta-surfaces affect radio performance, including their interaction with path planning under realistic constraints.

Objectives:

develop reduced-order, physics-informed surrogate models for predicting far-field communication quality metrics (e.g. RSSI, SINR) in urban IAM scenarios
enable real-time, on-board estimation of radio-coverage and communication performance during Vertical Take-Off and Landing Capable Aircraft (VCA) operation
integrate coupled multi-physical FE simulation results (electromagnetic, thermal, mechanical) into surrogate model training
demonstrate applicability of the surrogate models in joint optimization of c...