doctoral contract (M/F) : Minimalist Multisensory Approach for Robotic Navigation: Toward Multi-Sensory Familiarity combining minimalist vision and electric sense (H/F)

Job Description

Thesis Subject

Problem Statement
Autonomous navigation in underwater environments is a major technological challenge, especially in the absence of satellite positioning systems (GNSS) or communication infrastructure. Current solutions, such as sonar, are often energy-intensive, costly, and active — making them easily detectable. Minimalist vision can be used to memorize and retrace paths (Gattaux et al 2025, 2026), but underwater, turbid water frequently limits its effectiveness. One innovative idea is to learn and repeat a path using both electric sensing and underwater vision, combining their strengths to improve robustness and flexibility.

Approach
This PhD project draws inspiration from the visual strategies of invertebrates (e.g., ants, bees), which navigate with remarkable efficiency despite limited cognitive resources. Many crustaceans with compound eyes also appear to use visual familiarity-based processing to locate their shelters, such as those established underwater beneath rocks.
The visual familiarity principle (ultra-frugal AI), inspired by invertebrate cognitive mechanisms, relies on a minimalist encoding of visual information. Unlike classical computer vision approaches — which require significant computational resources and memory — this method captures and memorizes only the massive contrast combinations present in an image or sequence of images (panoramic or otherwise). These contrast combinations are encoded and stored in a vector of just 15,000 bits (Gattaux et al., 2025, 2026), called a MBON (Mushroom Body Output Neurons, referencing insect neural structures). Each MBON acts as a compact visual memory, encoding the essence of a scene without storing unnecessary details.
During the recall phase, a new image is encoded using the same principle and compared to the stored MBON(s). A familiarity index is then calculated, quantifying the similarity between the current image's signature and the stored visual signatures. This mechanism enables a robot to recognize previously explored locations with remarkable precision while using minimal hardware and energy resources. This approach — both frugal and robust — paves the way for autonomous navigation systems suited to constrained environments, where efficiency and sobriety are critical.
In robotics, electric sensing is directly inspired by biological mechanisms observed in electric fish. The principle involves (e.g., F. Boyer, V. Lebastard, see references below):
• Emitting electric fields: A robot generates a weak electric field via one or more electrodes.
• Detecting distortions: Conductive or insulating objects in the environment locally alter this field. One or more sensors measure these variations to locate, map, or identify objects.

The project will adapt the processing pipeline to memorize cues from both electric sensing and minimalistic vision, enabling path retracing through multi-sensory familiarity.


Job Profile
This multidisciplinary thesis requires strong expertise in several of the following areas:

Robotics, computer vision, control systems, dynamic modeling, signal processing, or machine learning.
Programming experience (Python, MATLAB) and handling of experimental data.
Interest in bio-inspiration, animal cognition, mobile robotics, or autonomous systems.

The candidate must hold one of the following degrees:
A Master's or engineering degree in robotics, AI, computer science, embedded systems, computer vision, or mechatronics.

Your Work Environment

Work Context
The selected candidate will be employed by the CNRS on a 3-year fixed-term contract at the Lab-STICC UMR6285 laboratory (labsticc.fr) on the ENSTA Brest campus (ensta.fr) and will be enrolled in the SPIN Doctoral School (ed-spin.doctorat-bretagne.fr).
Vincent Lebastard at IMT-Atl Nantes will co-supervised the PhD thesis.

This thesis is part of the PEPR Acc Robotique MiniRo (https://anr.fr/fileadmin/documents/2026/CP-PEPR-Robotique-2026-02-03.pdf )

Additional Information
Applications must be submitted via the CNRS portal (PDF format only) and must include:

A detailed CV highlighting relevant knowledge and experience.
A cover letter explaining your motivation and interest in the position.
Detailed academic transcripts (Bachelor's, Master's, or engineering degrees).
Full contact details (name, position, email, phone) of two references (e.g., internship or academic supervisors) who can be contacted directly.

Applications will be reviewed on a rolling basis until the position is filled. The expected start date is November 2026, with some flexibility.

For any questions, please contact F. Ruffier: franck.ruffier@cnrs.fr.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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