Opportunities

Post-doc “Flexible robotics and digital twins for cardiac surgery”

Context:

This postdoctoral position is part of the RHU-ICELAND project, involving several academic, hospital, and industrial partners. The objective of the project is to develop a new transfemoral mitral valve annuloplasty solution that integrates intracardiac ultrasound imaging and robotics. This approach allows intervention on a beating heart without extracorporeal circulation, offering a mitral valve repair solution for high-risk patients who are ineligible for open-heart surgery and, in the long term, for most patients requiring mitral valve repair.

Direct annuloplasty involves affixing a ring or band directly onto the mitral annulus using anchors under echocardiographic and fluoroscopic guidance. The advantage of this technique is that it constrains the shape of the mitral annulus, closely replicating surgical mitral annuloplasty. The RHU-ICELAND project focuses on two key phases: developing a numerical model of the anatomy and the robotic system used to apply staples to the mitral valve, followed by designing and evaluating the robotic system, which is validated through numerical modelling.

Scientific Objectives:

Initially, the recruited postdoc will focus on the numerical modelling of anatomical structures (veins, heart, mitral valve, etc.). Preliminary work has already been carried out to design a numerical model of the heart and mitral valve with opening and closing cycles. The aim is to enhance this model for greater realism. The model will be used for clinician training, preoperative intervention planning, and validating the geometric, kinematic, and dynamic models of the robotic system (active catheter) during navigation from the entry point (femoral vein) to the target site (facing the mitral valve). The other medium- and long-term goal is to develop a realistic and, above all, patient-specific numerical model, meaning constructing the numerical model based on the patient’s preoperative images.

The recruited candidate will work closely with academic and clinical teams involved in the project, particularly when integrating the research into the final demonstrator. The postdoc will benefit from a stimulating research environment and access to clinical data provided by the project’s clinical and industrial partners. They will also participate in project management (meetings, decision-making, report writing, etc.).

Host Institution:

The recruited candidate will join the Institute of Intelligent Systems and Robotics (ISIR) at Sorbonne University and CNRS (Paris). ISIR is organized into several multidisciplinary teams, including RPI-Bio. Research areas include microrobotics, drones, surgical robotics, bionic prosthetics, social robots, and various intelligent and interactive systems (physical, virtual, or mixed-reality), as well as artificial intelligence. Applications address major societal challenges: health, the industry of the future, transportation, and personal services.

The RPI-Bio team (robotics, perception, and interaction for biomedical applications), to which the postdoc will be attached, conducts research in healthcare robotics on topics such as interactive systems for expert guidance (surgery), perception (visual and haptic), human-machine interfaces, telemedicine, and microrobotics. Recently labelled by Inserm, RPI-Bio has extensive experience in developing advanced robotic solutions for interventional medicine (orthopaedics, neurosurgery, ENT surgery, endovascular interventions, etc.).

Profile Sought:

– Expertise in robotics, mechatronics, simulation, and/or numerical modelling

– Advanced programming skills (C++, MATLAB, Python)

– Proficiency in a numerical simulation library for soft robots (e.g., SOFA) is a plus

– Enthusiasm for interdisciplinary research and a collaborative spirit

General information :

– Supervisors: Lingxiao Xun; Brahim Tamadazte

– Contract start date: as soon as possible

– Contract duration: 12 months, renewable for a further 12 months

– Salary: depending on experience

– Host laboratory: ISIR (Institut des Systèmes Intelligents et de Robotique), Campus Pierre et Marie Curie, 4 place Jussieu, 75005 Paris

Contact person:

Send a single PDF file containing: a CV, a cover letter, and any scientific articles you deem relevant to the application to lingxiao.xun@sorbonne-universite.fr and brahim.tamadazte@cnrs.fr. Please include ‘post-doc rhu’ in the subject line of the email.

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Post-doc : Learning in robotics, with application to grasping

Context:

During the FET Proactive DREAM project (http://dream.isir.upmc.fr/) has been defined an approach for adaptive robotics based on open-ended learning. The main goal is to allow a robot to learn without requiring a careful preparation by an expert. This approach raises many challenges, notably learning with sparse reward, representation learning (for states and actions), model learning and exploitation, transfer learning, meta-learning and generalization. These topics are considered in simulation, but also on real robotics setup, notably in the context of grasping.

Missions:

This position aims at contributing to these topics in the context of several European projects, in particular SoftManBot, Corsmal, INDEX and Learn2Grasp. Calling upon previous works in the research team, the proposed approaches need to be easy to adapt to different robotic platforms and will thus be applied to different robots (Panda arm from Franka-Emika, Baxter, PR2 or TIAGO, for instance).

Required profile:

Candidates for the position must have a PhD degree in machine learning or related field in which robotics applications (either simulated or real) have been considered.

Required skills:

An excellent background is expected in machine learning as well as an experience in robotics. Excellent programming skills in Python are expected.

General Information: 

• Position Type: Post-doctoral researcher
• Contract duration: 24 months
• Level of education required: PhD
• Remuneration : Remuneration according to experience
• Location: ISIR (Institut des Systèmes Intelligents et de Robotique), Campus Pierre et Marie Curie, 4 place Jussieu, 75005 Paris.

Contact person: 

• Stephane Doncieux
• stephane.doncieux(at)sorbonne-universite.fr
• Send your application by email, with a CV and a cover letter.

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Internship subject: Engagement Detection for Human-Robot Interaction

Context:

Engagement detection is a critical prerequisite for socially appropriate human–robot interaction (HRI), enabling robots to decide when to approach a person, whether to initiate conversation, and how to do so without violating social norms or personal space. In everyday human interaction, engagement is communicated implicitly through posture, gaze, motion patterns, ongoing activity, and spatial arrange- ments, all of which inform whether an interaction is welcome. In HRI, these signals are closely tied to concepts of proxemics, privacy, and conversational readiness. Prior work has shown that robots which respect engagement cues and personal space are perceived as more intelligent, trustworthy, and less intrusive, particularly in shared and public environments.

Problem statement:

Most existing engagement detection systems rely on short-term changes in observable cues such as mo- tion in the visual field, head pose, eye gaze, or the presence of speech to infer engagement states. While effective in constrained settings, these approaches often treat engagement as a reactive signal derived from low-level sensory changes, rather than as a latent social and intentional state. As a re- sult, such systems struggle to distinguish between visually similar but semantically different situations (e.g., a person glancing toward a robot versus actively seeking interaction), and they generalize poorly across contexts, activities, and multi-person scenes. Moreover, cue-based approaches provide limited mechanisms for explicitly encoding privacy, “do-not-disturb” intent, or task-focused human behavior, increasing the risk of socially inappropriate or intrusive robot actions.

Objectives & Scientific approach:

The objective of this work is to reconceptualize engagement detection as an intention-aware world- modeling problem that integrates perception, prediction, and social reasoning. Rather than relying solely on instantaneous audio-visual changes, we aim to leverage video foundation models and self-supervised predictive learning to infer latent human states such as attention, intention, availability, and interaction readiness over time. Inspired by recent advances in world modeling and joint predictive architectures, this approach seeks to learn structured representations of human activity, spatial context, and social dynamics. This will allow a robot to anticipate whether an approach or conversation would be appropriate. By modeling engagement as a temporally grounded and context-dependent phenomenon, the robot can make proactive yet conservative decisions, initiating interaction only when the likelihood of acceptance is high.

Work plan:

The successful candidate will work on the following tasks:

– LiteratureReview:Analysisofstate-of-the-artmethodsinengagementdetection,proxemics,and video foundation models (e.g., VideoMAE, multimodal LLMs) ;

– Methodology Design: Developing a pipeline that utilizes video foundation models to extract tem- poral features regarding human activity and social context ;

– Implementation: Training/Fine-tuning a predictive model to infer ”interaction readiness” based on historical context rather than instantaneous cues ;

– Evaluation: Validating the approach on public HRI datasets or real-world scenarios, comparing against baseline cue-based methods.

Candidate profil: 

– Education: Master 2 student in Computer Science, Robotics, AI, or related fields.

– Technical Skills: Strong programming skills in Python ; Experience with Deep Learning frameworks (PyTorch or TensorFlow) ; Knowledge of Computer Vision (Transformers, Foundation Models) is a strong plus.

– Soft Skills: Autonomy, scientific curiosity, and good writing/communication skills in English.

How to apply:

Please send a CV and recent grade transcripts to Hamed Rahimi with the subject line ”[M2 Application] Engagement Detection Internship”.

More informations:

– Location: ISIR, Sorbonne University, Paris, France

– Team: ACIDE

– Supervisors: Hamed Rahimi (hamed.rahimi@sorbonne-universite.fr)

– Duration: 5-6 months

– Keywords: Human-Robot Interaction, Computer Vision, Foundation Models, World Models, Deep Learning.

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