Doctoral Researcher in Learning-Based Robot Motion

About us

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services.
We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications.

Your role

The SnT Automation & Robotics Research Group seeks to hire an excellent and motivated PhD candidate within the national research project PCS-GRAPHS (Integrating Situational Awareness, Planning and Control for Autonomous Robots using S-Graphs), funded by the Luxembourg National Research Fund (FNR).

The successful candidate will work under the main supervision of Prof. Holger Voos and will be required to perform the following tasks:

• Carry out the aforementioned research with national and international collaborators, e.g., INRIA Rennes, University of Zaragoza, TU Munich or University College London
• Disseminate your findings at renowned international robotic conferences and workshops such as e.g., ICRA, IROS, and in top-level journal papers
• Supervision of Master and Bachelor students contributing to the project
• Provide assistance in organizational matters related to the project PCS-Graphs

Since autonomous robots need to operate in complex and ever-changing environments for long periods, they must constantly understand their surroundings to make smart decisions and complete tasks, such as moving safely without collisions or handling objects. Recent methods for robotic situational awareness (SA), like our work on Situational Graphs (S-Graphs), improve on existing techniques by combining 3D environmental maps with detailed knowledge about objects into a single, optimized model. First novel solutions use parts of these models for planning or control, but they do not take full advantage of the structured, layered information such graphs so far. Therefore, our project aims to tightly integrate S-Graphs with motion planning and control.

For that purpose, we will first extend our SA models to XS-Graphs by including additional information necessary for motion planning and control, representing the situation with Probabilistic Graphical Models such as Factor Graphs based on the fusion of multimodal sensorial information and integration at different layers of abstraction. The major contribution of the PhD will be the derivation of multilayered approaches for motion planning and control based on the XS-Graphs, where both model-based and learning-based solutions are foreseen. This includes the development of a higher-layer semantic planning, the decomposition of the plan in subsegments and dedicated controller (such as MPC) for combined planning and control on a subsegment of the XS-Graph. This exploitation of the multilayered structure of the XS-Graphs will lead to a very efficient planning and control approach to outperform SOTA solutions. The overall approach will be tested and assessed in simulations, experiments and use case demos using our laboratories equipped with drones, legged and humanoid robots.

For further information, please contact Holger Voos at:

Your profile

• Solid experience / knowledge in the following areas:
• Solid background in motion planning and control of mobile robots
• Background in SLAM and SA models
• Background in Reinforcement and Deep Learning in robotics with a focus on planning and control
• Knowledge in Probabilistic approaches and Probabilistic Graphical Models would be an asset
• Outstanding academic records
• Teamworking experience, e.g. via student projects, competitions or similar
• International experience is desirable, e.g., via a study abroad, internships, Erasmus, or similar

Qualification: The candidate should possess an MSc degree or equivalent in Robotics / Computer Science, Mechatronics / Mechanical or Electrical Engineering, or related fields in Engineering, Computer Engineering or Applied Mathematics

Programming Skills: Programming Experience with Python, C/C++, ROS and ROS2 are essential, Matlab and AI/ML tools are an asset

Language Requirements: Applicants must demonstrate at least B2-level proficiency in the language of their thesis. For details and accepted certificates, please visit the Application for admission - Doctoral Candidates.

We offer

• Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the "University of the Greater Region" (UniGR)
• A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure
• A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …

How to apply

Applications should include:

• Curriculum Vitae including:
  • For each degree received or currently enrolled in, provide the degree, institution name, institution city and country, and date (or expected date) of graduation
  • List of publications (authors, title, journal/conference name and date of publication). Provide a link in case of open access
  • Portfolio of skills and projects
• Cover letter presenting your motivation for this doctoral thesis topic, and explaining how your qualifications and aspirations align with its academic focus
• Transcript of all modules and results from university-level courses taken

Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered.

All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students.

General information:

• Contract Type: Fixed Term Contract 36 Month, extendable up to 48 months if required
• Work Hours: Full Time 40.0 Hours per Week
• Location: Kirchberg Campus
• Internal Title: Doctoral Researcher
• Job Reference: UOL08057

The yearly gross salary for every PhD at the UL is EUR 41976 (full time).