Postdoctoral Researcher - Robust Control Architecture for Mobile Manipulator Robots

Offer Description

The ImViA laboratory (Imaging and Artificial Vision, UR 7535) is a research unit of the University of Burgundy Europe (UBE), recognized for its work at the interface between computer vision, image processing and control of physical systems. It brings together several thematic teams, including a team dedicated to vision-assisted control of physical systems, developing innovative approaches where visual perception is integrated as an active sensor in the control loop of complex robotic systems.

This team conducts research on robust control, state estimation by vision, visual servoing and machine learning applied to cyber-physical systems. It has instrumented experimental robotic platforms (cameras, mobile robots, manipulator arms) enabling real-condition validation of the developed algorithms.

The MMR project (Robust control Architecture for Mobile Manipulator robots) aims to propose solutions that can address a major challenge of modern robotics: enabling mobile manipulator robots to operate safely, robustly and efficiently in dynamic and weakly structured environments. These systems, combining an autonomous mobile platform and an articulated manipulator arm, find strategic applications in infrastructure maintenance, intelligent logistics, inspection, rescue in disaster areas and precision agriculture.

The originality of the MMR project lies in the synergistic integration of robust predictive control (Robust/Stochastic MPC), observers coupled with Physics-Informed Neural Networks (PINN) and Control Barrier Functions (CBF), with active exploitation of vision as a sensory modality for environment perception and real-time state estimation.

Research domain: Control of physical systems, mobile robotics and manipulation, robust control and real-time constrained optimization, computer vision applied to robotics.

Mobile manipulator robots represent a major evolution of modern robotics. Intrinsically nonlinear, strongly coupled and underactuated, they remain sensitive to external disturbances (ground irregularities, load variations) and internal uncertainties (variable parameters, approximate models), making their control particularly challenging under real-time constraints.

Three locks condition their effective deployment:

• Dynamic lock: the nonlinear coupling between the mobile base and the manipulator arm complicates control design and stabilization, particularly during simultaneous movements.
• Robustness lock: disturbances and uncertainties degrade precision and stability. Existing approaches do not allow robustness and energy efficiency to be guaranteed simultaneously [2].
• Safety lock: formally guaranteeing collision avoidance while satisfying multi-objective constraints in real time. Predictive controllers (MPC) struggle to integrate formal safety and robustness to unbounded uncertainties; pure machine learning lacks formal stability guarantees for critical applications [3].

The MMR project proposes a control architecture based on three principles:

• Robust predictive control (Robust/Stochastic MPC) to anticipate dynamic interactions and manage uncertainties proactively [4];
• Real-time estimation of disturbances and uncertainties by robust observers coupled with Physics-Informed Neural Networks (PINN), enriched by vision data (obstacle detection, pose estimation, scene reconstruction);
• Formal safety guarantee via Control Barrier Functions (CBF) integrated into optimization constraints [5].

References: [1] Khan & Thomas, 2026 — [2] Gao et al., 2026 — [3] Heins & Schoellig, 2023; Ames et al., 2019 — [4] Guo et al., 2026 — [5] Thirugnanam et al., 2022.

Job description and activities

• Develop and validate a robust control architecture for mobile manipulator robots integrating MPC, adaptive observers and CBF, with exploitation of vision as a sensor in the control loop.
  • Design and implement robust observers coupled with Physics-Informed Neural Networks (PINN) for real-time estimation of disturbances, uncertainties and system state, leveraging available visual data.
  • Integrate Control Barrier Functions (CBF) as optimization constraints to formally guarantee collision avoidance, exploiting visual perception of the environment.
  • Carry out simulations under MATLAB/Simulink, then experimentally validate the developed algorithms on the instrumented robotic platforms of the ImViA laboratory.
  • Actively contribute to the scientific dissemination of results: writing articles in rank A international journals and presenting at national and international conferences.
  • Participate in project meetings, occasionally supervise master's students and contribute to the scientific life of the ImViA laboratory.

Relation scope:

The postdoctoral researcher will work in close collaboration with:

• The MMR project leader within the ImViA laboratory (direct scientific supervision)
• The members of the Physical Systems Control and Vision team at ImViA (researchers, PhD students, engineers)
• The robotic and vision experimental platforms of the laboratory

Where to apply

Website: https://ube.nous-recrutons.fr/poste/gaigrod48a-post-doc-architecture-de-commande-robuste-pour-robots-manipulateurs-mobiles/

Requirements

• Research Field: Engineering » Electronic engineering
• Education Level: PhD or equivalent

Skills/Qualifications

Candidate holding a PhD in automatic control, robotics, image processing or a related field (electrical engineering, computer science applied to systems), obtained less than 3 years ago. The PhD degree is mandatory at the time of taking up the position.

Essential technical skills:

• Proven expertise in nonlinear control and robust predictive control (MPC)
• Proficiency in simulation tools: MATLAB/Simulink
• Skills in constrained optimization
• Experience in modeling and simulation of complex robotic systems
• Demonstrated ability to produce high-level scientific publications

Desired technical skills:

• Knowledge of formal safety guarantees (Control Barrier Functions, Lyapunov)
• Experience with Physics-Informed Neural Networks (PINN) or data-driven methods applied to control
• Skills in computer vision applied to robotics: visual servoing, pose estimation, object detection
• Experience in experimental validation on real robotic platforms

Transferable skills:

• Autonomy, scientific rigor and ability to conduct research independently
• Ability to integrate into a multidisciplinary environment (automatic control, vision, robotics)
• Ability to present and disseminate scientific results in written and oral form

Specific Requirements

Language

• French: Good — B2 minimum (daily interactions in the laboratory)
• English: Excellent — C1 minimum (scientific writing, presentations at international conferences)

• Languages: FRENCH — Level: Good
• Languages: ENGLISH — Level: Excellent
• Research Field: Engineering » Electronic engineering
• Years of Research Experience: None

Additional Information

Benefits

• Working conditions and particular constraints: No particular conditions. Occasional travel possible for international conferences and meetings between the two laboratory sites (Le Creusot and Dijon).
• Geographical location: ImViA Laboratory — University of Burgundy Europe (UBE), Le Creusot site (71200), France.
• Rights and obligations, including ethical and intellectual property obligations: The beneficiary (postdoctoral researcher) will be bound by professional secrecy with respect to third parties, not only regarding the laboratory activities related to the field of study and research, but also regarding any other laboratory activities of which they may become aware. They may not make any publications or written or oral communications relating to their research without the prior agreement of the Scientific Manager of the project. The postdoctoral researcher's publications and communications must necessarily be co-signed by the scientific supervisor of the research, who is a full member of the laboratory. The results of the research, whether "patentable" or not, will be the full and entire property of the laboratory, which may file patents in its name to protect the inventions made.

CONTRACT

• Category: A
• Anticipated contract start date: 01/01/2027
• Contract duration: 12 months
• Full time
• Remuneration: From €2,417 gross monthly

Selection process

• The application file includes:
  • A complete CV (educational background, certifications, professional and academic experience, most significant publications, internships completed…),
  • A motivation/application letter
  • The thesis defense report
• Application deadline: 15/11/2026

Additional comments

The recruitment process timelines may be impacted by the candidate's nationality (administrative procedures with prefectures and consulates) as well as by the context of the assigned laboratory (restricted access zone ZRR, sensitive unit, etc.) which may require the prior opinion of the Defense and Security Officer (DSO).

Website for additional job details: https://imvia.ube.fr/laboratoire/

Work Location(s)

• Number of offers available: 1
• Company/Institute: Laboratoire ImViA - Site du Creusot
• Country: France
• State/Province: Côte d'Or
• City: Creusot
• Postal Code: 71200

Contact

• City: DIJON Cedex
• Website: http://www.u-bourgogne.fr/
• Street: 9 Avenue Alain Savary - BP 47870
• Postal Code: 21078