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TU Berlin PhD Positions (Fully-Funded) in 6G, Edge Computing, Localization and Machine Learning for Networking 2021

TU Berlin has open, fully-funded PhD positions in 6G, Edge Computing, Localization, and Machine Learning for Networking.

Application Deadline:

Not specified

About the TU Berlin PhD Positions:

For the TU Berlin PhD Positions, we are looking for talented graduates of Computer Science, Computer Engineering, Electrical Engineering, or related field who wants to pursue his or her studies towards a PhD at the School of Electrical Engineering and Computer Science, TU Berlin, Germany. The positions are part of the Telecommunication Networks group headed by Prof. Falko Dressler.

Our research objectives include adaptive wireless networking (sub-6GHz, mmWave, visible light, molecular communication) and wireless-based sensing with applications in ad hoc and sensor networks, the Internet of Things, and Cyber-Physical Systems. Our current projects focus on novel and innovative concepts for 5G/6G communication systems, digital smart cities of tomorrow, and vehicular applications, emphasizing on communication technologies and cooperative assistance systems.

Eligible Field(s):

The candidate for the TU Berlin PhD Scholarship is expected to contribute to the ongoing research activities in at least one of the following fields.

• edge computing and network virtualization
• wireless ranging and localization
• channel measurements and modeling
• reinforcement learning for wireless communications

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Research Focus

Heterogeneous Wireless and Mobile Systems
We are working on methods ranging from coexistence to collaboration in unlicensed spectrum bands. Here, we are particularly interested in new radio resource and interference management approaches as well as techniques for self-organization and self-adaptation.
Cross-technology communication (CTC) is a means to bridge the gap between devices and standards. This is becoming particularly relevant for enabling control channels using between heterogeneous networks and to support IoT devices using narrow-band communication from wide-band OFDMA-based systems.
We also make use of RF-signals for indoor and outdoor localization. The challenge is the fusion of localization information from diverse sources such as inertial measurement units (IMU). In this line of research, we are also interested in joint communication and sensing.

Beyond RF / Unconventional Communication
Going beyond standard sub-6GHz channels, we are working on line of sight communication technologies such as millimeter wave (mmWave) and visible light communication (VLC). Research challenges include the characterization of channels based on empirical measurements to develop realistic simulation models, as well as the design of heterogeneous communication protocols using VLC and/or mmWave in combination with sub-6GHz. We are also investigating joint communication and sensing using both mmWave and VLC.
Going well beyond conventional communication, we explore nano communication and molecular communication channels. This includes the integration of in-body nano communication and body area networks for healthcare applications. Here, ultrasound and particle dispersion are good alternatives to electromagnetic communication. Macroscopic molecular communication, in contrast, helps establishing communication in industrial environments such as water tanks, and even allows studying the duality of air-based molecular communication and pathogen distribution through aerosols.

Intelligent Transportation Systems (ITS)
The field of vehicular networking (V2X) and intelligent transportation systems has been on our research agenda since a long time. More recently, we are interested in safety systems for vulnerable road users. Here, human-in-the-loop testing in our virtual cycling environment can be the key for next generation safety solution.
Platooning, i.e., cooperative automated driving of vehicle convoys, needs to be supported by adaptive heterogeneous communications and optimal platoon formation algorithms.
Vehicular micro clouds help to support MEC in 5G networks and beyond. These micro clouds are made of cars dynamically joining and leaving the cluster. Offloading can now be performed in a fully distributed way, still supported by the cloud and data centers.
Our research strongly requires holistic V2X Simulation. We are investigating real-time V2X simulation as well as the integration of connected electric mobility.

Machine Learning and Networking
Wireless communication networks have evolved into complex and dynamic systems. The numbers of knobs to be optimized are huge, e.g., in WiFi and LTE standards. Thus, unconventional approaches needed to master the complexity of control and management. We explore machine Learning algorithms like reinforcement learning (RL), which are very well suited for such applications. RL is particularly promising for hard problems like co-existence of heterogeneous technologies. Still, challenges like long learning time need to be solved. In a further step, we study transfer learning for training in simulation and use of the trained models in real world. We developed multiple Open AI Gym-based toolkits, e.g., ns3gym (network simulation), grGym (software defined radio), and VeinsGym (intelligent transportation systems).

Research Methodology

Modeling, simulation, and experimentation
Our research methodology is covering the full range from modeling to simulation to real-world experimentation. To support the research community, we make almost all developed software and measurement data available as Open Source and Open Data, respectively.
In the domain of simulation-based performance evaluation, we are particularly proud of our widely used frameworks. This includes Veins, Plexe, and VeinsGym for vehicular networking, EVI for Real-time coupling with ego cars, VCE for integrating vulnerable road users, and Pogona for simulating molecular communication.
As a systems-focused research group, we build systems – at least prototypes – for real-world experiments and field tests. We make use of COTS platform-based systems using standard chipsets for WiFi5 and WiFi6 experiments; we developed OpenC2X, an embedded Linux based platform for standard-conform field tests; and we provide libraries for GNU Radio-based software defined radio such as gr-ieee802-11 and gr-ieee802-15-4.

Type of Award:


Eligibility for the TU Berlin PhD Positions:

  • Applicants should have earned an M.Sc. degree in computer science, computer engineering, electrical engineering, or in a related area, and have demonstrated prior achievements in any of the above areas.
  • All applicants should possess excellent written and oral communication skills in English, a strong mathematical background, and programming experience. Enthusiasm for leading-edge research, a mix of creativity and persistence, and team spirit are essential.

Selection Criteria:

  • A deep understanding of wireless networking principles is required; experience one of the above mentioned research domains is considered a plus for the PhD Positions. We expect the candidate to have programming experience (Python, C++, Matlab) and general Linux expertise.
  • Successful candidates for the TU Berlin PhD Positions are expected to contribute to research projects and teaching as active members of a young team. In return, we provide a relaxed and inspiring working atmosphere allowing you to address interesting and challenging research problems.

Eligible Countries for the TU Berlin PhD Positions :


To be Taken at:

TU Berlin, Germany

Number of TU Berlin PhD Positions:


Value of TU Berlin PhD Positions:

The salary is very competitive; according and to the German TV-L 13 system (roughly 2,200 Euro after tax)

How to Apply for the TU Berlin PhD Positions:

Interested applicants should send a letter of motivation, a curriculum vitae, scanned transcripts, and contact information of at least two references electronically to Prof. Falko Dressler using the subject line “PhD @ TKN”.

  • It is important to go through all application requirements in the Award Webpage (see Link below) before applying.

Visit TU Berlin PhD Positions Webpage for Details

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