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-T8/2
Title: Multimodal Navigation with Adaptive Sensor Fusion and Localized Map Management
Supervisor: Jun.-Prof. Dr. Anette Eltner, Junior Professorship in Geo Sensor Systems, Prof. Dr. Hans-Gerd Maas, Chair of Photogrammetry and co-supervised by at least one additional professor plus an international tutor of the RTG

Description of the PhD topic:
Operating UAVs in urban areas puts special requirements on the accuracy and reliability of navigation data. On the other hand, this data is impaired by effects such as signal shadowing. The project aims to combine complementary navigation methods and develop situation-dependent integration solutions.
This PhD project focuses on adaptive semantic fusion of multiple navigation sources for Innovative Air Mobility (IAM), including GNSS, high-precision MEMS-based INS, 5G/6G communication with V2X and edge computing, and vision- and LiDAR-based odometry using semantic landmarks. Accurate localization is challenged by GNSS shadowing, 5G multipath effects, and variable sensor quality, which can significantly impact the safety of flight. The thesis shall develop robust state estimation methods by combining factor graph-based sensor fusion, variance component analysis, and modern deep learning approaches such as adaptive Kalman filters and transformer architectures. Integration of object detection and landmark recognition with 3D city models may enable semantic awareness for navigation. Efficient map compression and dynamic level-of-detail switching may ensure computationally tractable on-board processing, while sensor cascades shall be optimized for varying conditions including night or convective weather. The project aims to deliver a reliable, adaptive navigation framework that supports safe and precise UAM operations in complex urban environments. Objectives:

develop adaptive multi-sensor fusion techniques combining GNSS, INS, 5G/6G, vision, and LiDAR to achieve robust real-time localization for UAM
incorporate factor graph-based fusi...