Company Description

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) is a tech research hub at the University of Luxembourg where over 500 brilliant minds work on solving real-world problems. SnT focuses on cybersecurity, space systems, autonomous vehicles, and fintech solutions, partnering with companies to turn research into competitive business solutions since 2009. Scientists at SnT shape the digital future of Luxembourg and beyond by turning ideas into reality.

The SpaceR team is a young and vibrant team of over 27 members, fostering a collaborative atmosphere (1 Professor, 1 Research Scientist, 15 PhD students, 7 PostDocs, 3 Research assistants). SpaceR’s state-of-the-art facilities include the LunaLab and Zero-G Lab, dedicated environments for researchers to test their results on advanced equipment. The group works across the entire spectrum of space robotics research and applied research to conceive and demonstrate future paradigms on lunar exploration, anomalies/failures identification and prediction, in-orbit operations, assembly and manufacturing, active and passive space debris removal, XR immersive teleoperation, and multi-robot cooperation.

Your role

The successful candidate is expected to take a leading role in defining, acquiring, managing, and scientifically contributing to projects around AI-enabled space-borne perception systems for robotic proximity operations in collaboration with Redwire Space Luxembourg. The candidate will carry a leading role in this area and support PhD candidates in their thesis research. The candidate will work closely with Prof. Olivares-Mendez and Dr. Carol Martinez, the members of the Space Robotics (SpaceR) research group (www.spacer.lu) and Redwire Space Luxembourg (https://redwirespace.com/).

The candidate will lead the development of a V&V framework for AI-augmented perception systems and will be responsible for activities in the Zero-G Lab and contributing to the Black-Hole Lab at Redwire Space Luxembourg for Hardware in the loop emulation of on-orbit scenarios.

Responsabilities:

Investigate existing verification and validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft .
Develop novel methods for characterization and quantification of input-output bounds in an AI-augmented perception system: Develop novel methodologies and tools for characterizing and/or quantifying the performance envelope, robustness, and safety properties of perception systems that encompass learning-based models.
Develop methods and tools for automatic/procedural generation of relevant operational conditions
Inform and educate certification processes for such systems for the industrial partner: Explore approaches to generate safety cases and evidence suitable for contributing to the potential certification of these AI components according to relevant space standards.
Lead SITL and HITL Test campaigns: Implement algorithms and conduct experiments using both synthetic and potentially real datasets/simulators relevant to space environments.
Teamworking: Collaborate closely with team members at SpaceR and Redwire Space

Your profile

PhD in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, Aerospace Engineering or a related field, with a focus on Robotic Perception and learning based methods
Demonstrated expertise in at least one of the following areas:
Machine Learning / Deep Learning (particularly Computer Vision or 3D perception)
Verification & Validation (V&V) of advanced algorithms, software or systems
Formal Methods
Safety Engineering / Safety-Critical Systems
Control / Estimation Theory
Intermediate programming skills in Python and C++
Intermediate-level skills and proven experience with PyTorch
Working knowledge of ROS and previous experience of handling robotic experiments
Proven track record in relevant conferences and journals: At least three publications in top-tier venues: IROS, ICRA, RSS, R-AL, TRO, TFR, etc
Fluent written and verbal communication skills in English are mandatory
Commitment, teamwork and a critical mind

The ideal candidate should have the following skills and proficiencies:

Rapid learner and ready to push the frontier of space-borne perception systems
Strong grasp of the state of the art in machine learning for perception or safety in machine learning or GNC systems
Specific knowledge of the dynamics of spacecraft/space manipulator systems or online/offline certifiability of ML models
Experience with optical navigation or perception techniques (classical or learning-based) for space applications
Proven experience with Hardware and Software in the Loop testing of perception systems for space
Experience bridging Machine Learning with V&V, Formal Methods, or Safety Engineering