Inria, France invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Inria, France.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Multi-Fidelity Scientific Machine Learning with Heterogeneous Inputs
The objective is to design scalable multi-fidelity methods capable of merging information coming from models with mismatched parameterizations and heterogeneous data structures.
The PhD will investigate i) how to define common latent representations linking heterogeneous input spaces across fidelities; ii) how to build multi-fidelity surrogates that remain consistent under such heterogeneities; iii) how to quantify and propagate uncertainty induced by limited high-fidelity data and representation mismatch, iv) how to design adaptive sampling strategies for selecting expensive high-fidelity evaluations.
Deadline : 2026-08-01
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Decision-Dependent Learning Games and Quantal Stackelberg Equilibria: Incentives for Frugal Data Markets
The objective is twofold: to contribute to the theoretical foundations of hierarchical games with endogenous learning, and to develop tools for the design of incentive mechanisms in data markets, in particular to encourage more frugal and responsible uses of data. Through this positioning, the thesis aligns with key research priorities related to data governance, platform economics, and the sustainability of digital technologies.
Deadline : 2026-06-07
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Dynamic Approximate Computing for Energy-Efficient AI Hardware Accelerators
This research explores the principles and practical implications of approximate computing as a pathway toward more energy-efficient AI hardware accelerators. It examines how different forms of approximation affect computational efficiency, prediction accuracy, and overall system-level performance. Rather than treating these techniques in isolation, the study considers their combined impact across the computing stack, with particular attention to how accuracy-efficiency trade-offs can be characterized and controlled.
A central theme of the work is the integration of hardware and software perspectives through a co-design approach. By closely aligning algorithmic characteristics with architectural features, the research aims to uncover strategies for embedding approximation mechanisms directly into accelerator designs. Emphasis is placed on adaptive and context-aware approximation techniques that can dynamically balance energy savings and output quality, ensuring that efficiency gains do not compromise application-level requirements.
To ground these ideas in practice, the research involves modeling, simulation, and experimental prototyping using representative AI workloads, including deep learning inference and computer vision applications. Through systematic evaluation and validation, the study aims to assess the feasibility, robustness, and scalability of proposed approaches, contributing insights into the design of next-generation energy-efficient AI systems.
Deadline : 2026-05-31
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Scalable Reliability Assessment Under Natural Faults and Adversarial Perturbations
The goal of the PhD is to provide a unified framework for analyzing the impact of fault injection on microarchitectural security and reliability, with a focus on RISC-V processors and domain-specific accelerators. To achieve that, it is expected to create realistic models for the environmental faults and attacks under study, taking into account as much as possible the microarchitecture. Then, methodologies needed to evaluate system security and reliability will be proposed, along with post-analysis techniques to identify the most vulnerable components. In particular, a hybrid approach will be investigated, combining the strengths of formal methods and multilevel fault-injection simulation to address scalability challenges in complex systems when verifying these non-functional properties. An implementation of this methodology, possibly using the Circuit Intermediate Representation (IR) Compilers and Tools (CIRCT) framework [CIR], will be developed and used to guide the exploration of the architectural state space with respect to reliability and security metrics over a set of hardware blocks (such as Comet processor [SR22] for instance). Post-analysis techniques will also support the development of potential countermeasures.
Deadline : 2026-05-31
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Construction of a Generic Multilingual, Multi-Speaker Articulatory Model Using Real-Time MRI Data (PhD offer)
Speech production requires control of the movements of the articulators (jaw, tongue, lips, etc.) that are used to modify the shape of the vocal tract, and consequently the acoustic properties, including the resonance frequencies of the vocal tract.
When learning to speak or acquiring a second language, speakers learn to move and control their articulators to produce the sounds of their language. Articulatory synthesis mimics this process by using the temporal evolution of the vocal tract shape and source parameters as input. The advantage of articulatory synthesis is that it can explain the articulatory origin of phonetic contrasts, manipulate the movement of articulators (or even block one to simulate a speech impairment), adapt to a new speaker by modifying the size and shape of the articulators, and finally, reconstruct the vocal tract shape from the speech signal.
Compared to other synthesis approaches that offer high quality, the main advantage of articulatory synthesis is therefore its ability to control the entire speech production process.
Generating the geometric shape of the vocal tract at every moment of synthesis is the central focus of articulatory synthesis. It most often relies on the use of an articulatory model [1, 2] that determines the shape of the vocal tract using a small number of parameters. This model is almost exclusively constructed either from geometric primitives or from a small number of MRI images of a single speaker and, consequently, for a single language. Recently, we developed a model that uses a large number of dynamic MRI images of a speaker to generate the shape of the vocal tract based on a sequence of phonemes to be articulated [3].
Deadline : 2026-05-31
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Multi-tenancy in resource-constrained fog/edge platforms for natural environment observation
This PhD thesis topic integrates in a multi-disciplinary research effort between hydrologists who are interested in monitoring, studying and modeling the behavior of natural environment, and computer scientists who are interested in developing a fog/edge platform to support the computational requirements of natural observatories. It follows previous work on the LivingFog platform which was recently deployed in the university campus in Rennes as well as in the Himalaya mountains in Nepal [1]. The PhD student will be co-supervised by a hydrologist (Laurent Longuevergne) and a computer scientist (Guillaume Pierre) considering that the expected research lies at the boundary between the two research domains. On the one hand the thesis aim to address specific needs experienced by existing natural environment observatories and inventing solutions will require a deep understanding of the expected usage by the hydrologists; on the other hand, the thesis will also aim to address broader questions relevant in the domain of fog and edge computing out of the environment monitoring use case.
Deadline : 2026-05-13
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Mechanistic Interpretability and Problem-Space Adversarial Attacks for LLM-based Software Vulnerability Detection
Large Language Models (LLMs) have demonstrated remarkable capabilities in automating the detection of software vulnerabilities (SVD) due to their ability to process both natural and programming languages. However, a critical reliability concern with state-of-the-art LLMs is their susceptibility to adversarial attacks. Subtle, problem-space modifications to source code—such as variable renaming or dead code insertion—can mislead the model without changing the code’s main functionality or underlying vulnerabilities. Furthermore, the opaque, “black-box” nature of LLMs makes it difficult to understand whether they truly grasp code semantics or simply recognize superficial statistical artifacts.
Deadline : 2026-05-13
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Computational approaches for knowledge graph mining and completion dealing with uncertainty
This PhD thesis takes place within the MetaboLinkAI ANR-SNF project, which aspires to revolutionize the analysis and interpretation of metabolomics data through a multidisciplinary approach that combines a comprehensive knowledge graph hub (MetaKH) with cutting-edge artificial intelligence (AI) and machine learning (ML) techniques. The project’s main goals are to enhance the querying and ease of use of metabolomics data, improve research efficiency, and stimulate creativity in the field. These objectives are set to surpass current standards by creating an encyclopedic and expandable knowledge base, integrating advanced AI to handle the uncertainties of experimental data, and enabling a broader range of hypothesis testing and evaluation.
Within this project, we will focus on developing innovative methodologies and tools, such as graph mining methods, to enhance data interaction, analysis capabilities, and representation of uncertainty.
One distinctive peculiarity of metabolomics data (and thus MetaKH) is incompleteness, variable confidence and inherent uncertainty. Here, we adopt AI to enhance the completeness and reliability of the KG and to correctly account for uncertainty.
Deadline : 2026-05-08
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Automatic Detection of Meaning Misalignment in Dialogue for the Evaluation and Improvement of Conversational AI
This thesis is guided by the following overarching question: How can observable episodes of meaning-related misunderstanding in human conversation inform the modeling of lexico-semantic misalignment and its resolution, and how can this knowledge be transferred to conversational AI systems?
The first research direction concerns data collection. The goal is to obtain larger and more diverse datasets of conversational episodes in which speakers explicitly signal a difficulty related to word meaning. Building on existing resources and approaches to WMN indicator detection, the thesis will seek reliable (semi-)automatic ways of identifying such episodes across different conversational settings, registers and modalities. This will result in new annotated resources to be used in subsequent experiments and which will be shared with the scientific community.
The second direction will focus on data analysis. This will involve an exploration of the kinds of word usages that tend to create miscommunication (Garí Soler et al., 2025b). Relying on the collected WMNs as well as other relevant sources, such as suggested reformulations in instructional data (Anthonio et al., 2020), annotation disagreements revealing ambiguity (McCarthy et al., 2016), or scare quoted usages (Garí Soler et al., 2026), the thesis will seek to characterize lexical and contextual configurations that frequently lead to misunderstandings, with the goal of developing computational tools capable of identifying these usages, both to anticipate them in production and to trigger clarification when needed. This line of work will also investigate how speakers negotiate word meaning and how alignment emerges in conversation. Beyond the negotiation sequence itself, it will examine how the rest of the interaction reflects successful or unsuccessful alignment (Garí Soler et al., 2023).
The third direction concerns application to conversational AI. The insights and tools derived from data collection and analysis will be applied for the direct evaluation and improvement of conversational AI systems. The work will first aim to assess the capabilities of current language models and dialogue systems to handle and detect problematic word usages, respond to or produce clarification requests targeted to specific lexical items, and their ability to coordinate with a speaker after a lexical pact has been established. These experiments will reveal the weaker aspects of current systems and guide the development of models and tools that better emulate natural human communicative behavior in situations involving meaning unclarity.
Deadline : 2026-05-07
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Entanglement, non-Gaussianity, and the limitations of classical simulation of continuous-variable quantum systems
Quantum information processing promises considerable advantages over classical information processing, especially for computation, cryptography, communication, and sensing. In recent years, alternative approaches to quantum information processing in which bosons are the carriers of information have attracted increasing attention, because they offer a viable path to fault-tolerance and scalability. For instance, bosonic modes of light in quantum optics allow for the deterministic generation of the largest entangled quantum states to date, over a million of addressable subsystems, while bosonic modes of superconducting microwave cavity fields coupled to circuit quantum electrodynamics (QED) provide exciting prospects for quantum error-correction. Regardless of their underlying architecture, identifying what makes quantum computers more powerful than their classical counterparts is a very active area of research. These are fundamentally quantum properties such as entanglement, contextuality and non-Gaussianity, to name but a few. These properties of physical systems, which are indispensable to any quantum advantage over classical computers, are known as quantum computing resources. Their theoretical understanding is of major importance for the development of quantum computing technologies.
Deadline : 2026-05-07
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Group key management in decentralized collaborative systems
This PhD project aims to investigate the security and resilience of the Delivery Service component in the MLS architecture, with the objective of designing a decentralized and Byzantine-resilient Delivery Service suitable for large-scale collaborative systems.
First, we aim to analyze the security limitations of the current centralized Delivery Service model used in MLS deployments. Using both existing implementations and formal verification techniques, we will demonstrate that the centralized architecture can be vulnerable to several classes of attacks, including message ordering manipulation, consistency violations, and denial-of-service scenarios.
Second, we will design a decentralized Delivery Service architecture capable of coordinating message exchange among MLS clients without relying on a single trusted infrastructure. This architecture will aim to maintain the key consistency properties required by MLS while tolerating failures or malicious behavior of some participants.
In particular, we will focus on achieving resilience against Byzantine faults, where nodes in the system may behave arbitrarily or maliciously. Our goal is to develop a Delivery Service design that guarantees:
- consistent ordering of MLS messages,
- agreement among group members on the sequence of operations,
- robustness against malicious participants,
- scalability to large and dynamic groups.
Deadline : 2026-05-07
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Dynamics-Aware Graph Transformers for Predicting Allosteric Communication
Biomolecular function depends on both structure and dynamics. In particular, allostery is a mechanism in which a local event, such as ligand binding, produces functional effects at distant sites in a molecule. This process plays a key role in regulating biological systems and represents an important mechanism in many diseases. However, current computational approaches remain largely limited to static representations of biomolecules, limiting our ability to fully understand conformational heterogeneity and long-range communication.
Recent advances in artificial intelligence have transformed structural biology, yet most existing models still rely on static structures and fail to capture the dynamic nature of biomolecular systems. To address this limitation, our group has developed several key resources and methodologies:
- DynaRepo, a database of molecular dynamics trajectories of more than 700 macromolecular complexes (~5.5 billion frames) [1], and the first MDDB node in France
- ComPASS, a graph-based method for identifying communication networks in protein–protein and protein–nucleic-acid assemblies [2]
- DynamicGT, a dynamics-aware graph transformer for predicting binding sites in flexible and disordered regions [3]
Building on these foundations, the DynaNova project aims to leverage large-scale molecular dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes.
The PhD candidate will play a central role in this effort by developing new deep learning approaches that combine:
- Large-scale molecular dynamics data (via DynaRepo)
- Topological and geometric representations of molecular structures
- Advanced deep learning architectures (graph neural networks and transformers)
The objective is to learn conformational heterogeneity directly from molecular dynamics simulations and to identify and predict allosteric communication pathways across diverse biological systems.
Deadline : 2026-05-02
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Geometric-Semantic Adaptation of Multimodal LLMs for High-level Landmark Detection in Complex Environments
The research of this PhD will be articulated around the concept of useful landmark for localization in complex environments. Indeed, unlike cases where object detection or segmentation methods are used with no other objective than their own, using objects as landmarks for localization introduces specific constraints in terms of repeatability across different viewpoints, distinctiveness with respect to other landmarks, geometric accuracy and adequate distribution within the environment.
To address these challenges, we propose to exploit the possibilities offered by MLLMs (e.g., BLIP-2, LLaVA, MiniGPT-4), able to follow instructions or answer questions about an image, to extract localization information from images. More precisely, we want to examine how their general-purpose detection and segmentation abilities can be redirected towards automatically identifying high-level localization landmarks in specialized environments. For that, we first propose to assess both geometric and semantic sensitivity of different MLLMs to different combinations of visual and textual prompts, in order to derive automated prompting strategies. In particular, we want to study integration of 3D geometric and fine-grained semantic information within the prompts, and assess geometric accuracy of corresponding models’ answers. If necessary, we will then propose dedicated learning strategies for inducing the desired geometric capabilities within the model. In a second phase, we want to examine potential complementarity between MLLMs and scene graphs built from images to combine localization methods with adequate scene modeling.
Deadline : 2026-05-02
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Cost and Performance-Efficient Caching for Massively Distributed Systems
The goal is to design cost- and performance-efficient distributed smart caching middleware that facilitates data exchange within and across data providers (and producers) and consumers (users) while considering the temperature of the data, its frequency, and the heterogeneity and dynamics of the infrastructure. Specifically, we aim to address these research questions:
- How can we seamlessly aggregate the caches of many distributed, heterogeneous machines?
- Where to place data across different sites/organizations (across the IoT to cloud continuum).
- Which data should be cached and for how long, how to resize the caches between different applications/users, when to empty caches, etc
- How to exploit data caches for data streams and how to efficiently share caches with hot data.
- In addition, it is important to study the right number of replicas to meet the users demands.
Deadline : 2026-04-30
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Formal Verification of Liveness in Distributed Systems using Reinforcement Learning
We are interested in developing algorithms for automatically proving the absence of livelocks or detecting livelocks bugs in distributed protocols using reinforcement learning algorithms. We suggest developing deep RL algorithms to analyze maximal wait times in distributed protocols. The wait time is the number of steps a process is executed before it gains access to a resource. There are no livelocks if the wait times are always finite. The work consists in modeling this problem as an RL problem, choosing the right rewards and RL algorithms, and making sure it scales to real implementations of distributed algorithms.
Here the RL agent chooses at each step the schedule, that is, which process to execute, whether there are packet losses etc. and observes the next global state of the system. It receives a reward of 1 at each step a process waiting to access a resource is executed but without accessing that resource. Thus, the distributed protocol can be seen as a game which the RL agent must learn how to play to exhibit the worst-case behavior.
The model and RL algorithms can be chosen either to attempt to prove the absence of livelocks and compute bounds on wait times, or to detect livelock bugs. The precise direction to be taken and the weight given to RL versus formal verification in this work can be chosen according to the student’s background and preferences.The work also includes an extensive bibliographic study, the development of the above algorithms, implementation and experiments.
Deadline : 2026-04-30
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Visuo-tactile control for manipulating deformable objects with robotic arms combining tactile perception and photometric visual servoing
To overcome these limitations, this PhD thesis will elaborate a new approach that directly utilizes photometric and depth information from an RGB-D camera as visual features for control, eliminating the need for predefined geometric models and features extraction. While direct visual servoing has been successfully applied for rigid object positioning [6-7], its application to deformable object shaping remains unexplored.
This research will develop:
- A physics-based model generated online during the manipulation task, combining data from tactile sensors (mounted on robotic hands) and dense visual feedback.
- A hybrid visuo-tactile control scheme, fusing dense visual data with sparse tactile information (e.g., contact points, applied forces) to ensure stable and controlled deformation, particularly for handling fragile objects.
- A novel shape servoing framework, integrating photometric-based interaction models to predict and control deformation in response to robotic manipulations.
- Dimensionality reduction techniques to optimize the representation of photometric shape features using projection-based approaches [8-9].
Deadline : 2026-04-30
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Distributed Dimensionality Reduction for Large-Scale Physical Simulations
The project will first focus on dimensionality reduction techniques, and in particular the standard PCA method. To fit the requirements imposed by the HPC setting, we will consider distributed incremental PCA methods [8], that work with streaming data split over many computing nodes. An important consideration in our context is that, unlike classical data stream, the data is not i.i.d. on the nodes, but stems from the domain partitioning imposed by the physics of the problem. The two main objectives are the following:
– Benchmark existing methods: This will require a thorough state-of-the-art review, as well as defining the
relevant metrics for evaluating data compression in physics simulations (communication/computation time/cost,
quality of the solution…). The benchmark will be realized with benchopt [3] and will benefit from the distributed
coding expertise of both supervisors.
– Designing new efficient methods: To account for the structure of physic simulations, we propose to investi-
gate how to efficiently leverage the inter-node communication to improve existing distributed PCA methods [5, 4]. The convergence of the proposed methods will be analyzed and we will provide tight convergence bounds.
While the initial focus will be on PCA, more advanced compression methods will be considered throughout the project, in particular with spatial compression [1, 2], mesh-based wavelets [7], or auto-encoders [6].
Deadline : 2026-04-30
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Efficient data structures and algorithms for processing massive 3D data
The goal of this PhD is to (i) investigate new data structures to read, compress and store the information contained in massive point clouds efficiently, and (ii) to rethink popular processing tasks so that they can operate at multiple scales directly from such data structures.
The candidate will study the potential of different space partitioning data structures that can be built efficiently in a hierarchical way and from which information can be stored and requested easily. He/she will also propose compression operations to convert clusters of input points into lightweight geometric objects, and clusters of these geometric objects into a single one. The choice of geometric objects will have to account for representation genericity, compactness and efficiency to connect and aggregate them. Prior work shows, for example, that planar components (which are frequent in urban environments) can be turned into a hierarchy of floating polygons with a limited loss of information. Similarly, the notions of “superpoints” [9] or covariance trees [11] could also be a solution for compressing non-planar components.
The candidate will also revisit some traditional point cloud processing tasks such as estimation of local geometric properties, surface reconstruction or primitive detection under the idea that the atomic geometric element is not a 3D point anymore, but a geometric object living at a given scale of the data structure. Continuing on the previous example with polygons and superpoints, planar shape detection could simply be addressed by selecting polygons in the hierarchy of the data structure, and surface reconstruction, by assembling the geometric objects with a space partition.
The candidate will also investigate the potential of the proposed data structures in recent 3D deep learning architectures which still largely suffer from scalability issues. In particular, the proposed data structures could be an effective alternative to the very coarse simplification of input point clouds [10].
Deadline : 2026-04-30
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Knowledge Graphs and Neuro-Symbolic AI for the Analysis and Prediction of User Exploratory Behavior in Visualization Systems
The objective of this PhD is to develop a neuro-symbolic framework based on knowledge graphs (KGs) to model and interpret user activity, enabling the extraction and dynamic refinement of interpretable exploratory behavioral patterns that can serve as a robust foundation for AI-assisted, user-adaptive visualization systems.
User activity refers to any information describing a user during their session interacting with a visualization system, whether through interactions with the system, sensors capturing biometric data, or user declarations [1]. The study of such activity is central to the field of analytical provenance [2]. It supports, among others, the evaluation of visualization systems, the validation of analytical results, and the recommendation of suitable views or tasks [3]. This is particularly relevant in exploratory contexts, where users navigate large and complex datasets without predefined goals and often rely on multiple complementary views to discover patterns and derive insights. The increasing volume and diversity of data and visualization techniques require flexible interaction paradigms that allow users to construct their own exploratory workflows. For instance, the eSTIMe visualization system [4], used as a case study in this work, enables users to dynamically instantiate visualization techniques and assemble dashboards tailored to the task at hand. While powerful, this flexibility is constrained by users’ limited time and expertise for effective configuration [5].
Deadline : 2026-04-30
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Spatio-temporal analysis of remote sensing data at large scales
The goal of this PhD is to (i) develop efficient methods for detecting 3D changes and expressing them with simple geometric shapes, and (ii) analyze the spatio-temporal distribution of these geometric changes at large scales (i.e. from city districts to the entire country).
In contrast to existing 3D change detection methods that mostly operate from 3D point clouds, e.g. [1,2], the PhD candidate will investigate, as first objective, change detection methods that directly operate from more concise geometric primitives such as planes and 3D polygons. This strategic choice is motivated by both efficiency reasons as point-based methods suffer from a low scalability and interoperability reasons as such geometric primitives will directly feed the building reconstruction methods of the JNFT project for efficient 3D model updates. The candidate will investigate methods for detecting planar variations in a pair of point clouds. One possible solution will be to adapt static mechanisms such as [3] by using similarity metrics between planar shapes, as proposed in [4] for 3D data registration. The candidate will also investigate data structures to efficiently organize and parse the detected planar changes, e.g. by using Level of Detail trees [5].
The second objective will be to evaluate the potential of these detected spatio-temporal variations for understanding evolution of urban attributes. In particular, the PhD candidate will develop models for analyzing the spatio-temporal distribution of planar shapes at large scales and seek potential correlations on a variety of attributes that characterizes the city evolution in terms of shape, physics or functionality. A first naïve approach will be to extend the statistical models developed in [6] for basic 2D building footprints with more expressive 3D planar primitives.
Deadline : 2026-04-30
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Flow-Based Interactive Data Visualization
This PhD project investigates a form of visualization that remains largely unexplored in research: flow-based visualization. In this approach, visual flows are used as metaphors to represent data, with quantities conveyed through dynamic visual elements that continuously move in front of the observer. For example, waste production could be represented by garbage moving along a conveyor belt or falling from the sky, while monetary income coulds be represented by an endless sequence of banknotes moving across the screen (see https://neal.fun/printing-money/ for an example). Note that flow-based visualization differs from flow visualization, a widely researched area whose purpose is to visualize flow data, not to use flow as a visual metaphor.
Our research group has already begun studying how requiring users to pan through visual content over extended periods can help communicate large magnitudes [Yang26]. Flow-based visualizations extend this idea by enabling the representation of quantities per unit of time (e.g., tons of waste per day) through uninterrupted visual flows. By representing inherently dynamic quantities (rates) with dynamic visual processes (flows), such visualizations may enable a more direct and intuitive understanding compared to conventional static representations.
Flow-based visualizations can be implemented on standard computer displays, as illustrated by the money-flow example above, but may also be particularly well suited to immersive media such as augmented reality, similar to the work of Assor and colleagues [Assor24]. This project will investigate both contexts.
Deadline : 2026-04-30
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Modélisation et extraction efficace des flux de contrôle et de données pour l’ingénierie dirigé par les modèles (H/F)
The objective of this thesis is to extract and model the control and data flows observed during the execution of a software application, in order to make them usable within the Moose software analysis platform. The work will propose a metamodel integrable into the Moose/FAMIX ecosystem to represent this dynamic information, as well as an analysis chain to instantiate and use models on open-source and industrial case studies.
Deadline : 2026-04-26
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD in Integrated and Printed Circuit Design for Sustainable and Ubiquitous Electronics
We are looking for an ambitious 3-year PhD candidate to join a European project, GAIA, studying the subject of biodegradable ultra low-power electronics. The position will start near the commencement of the project, 5-May 2026.
The GAIA project aims to design electronics for a ubiquitous and ecologically-aware Internet of Things. The project will solve this with a completely revolutionary approach to electronic design by using transient and biodegradeable materials. These new devices will be able to perform computation and communication with energy harvested from the environment and from ambient wireless signals. The PhD student will be part of the design and electronic characterization of these devices as well as proof-of-concept prototype electronics that will be manufactured in more traditional solid-state processes.
As part of this project, you will be expected to work closely with a multi-national European consortium consisting of experts in telecommunications, embedded systems, RFID, integrated circuits, and printed electronics. You will also be supported closely, day-to-day, with a researcher, an engineer, and an additional doctoral student researcher.
Deadline : 2026-04-23
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Foundation Models and Natural Language Interaction for Human-Robot Collaboration
Most work on VLM/LLMs for robotics focused on generating sequences of actions and plans from high level goals, offline, only targeting autonomous robots isolated from humans. A critical limitation to deploy VLM/LLMs for robots collaborating with humans is their ability to be used online, in a human-in-the-loop scenario, to generate suitable motions and “safe” robot policies.
Here, we use VLM/LLMs to generate a robot’s motions online in collaborative scenarios where safety is critical: active exoskeletons and mobile manipulators assisting humans in object manipulation. The human vocally commands the robot interactively, online, to control the generation of its motion at the low level: start, stop, direct, and change its low-level parametrization (e.g., compliant behavior, the velocity, the maximal torque assistance, etc.).
Extension of paradigms and comparison with existing and fine-tuning of VLAs is also considered, as this is part of the ongoing research of the team.
The first objective is to design the robot’s controller with the natural language interaction feature in mind: the human’s commands, corrections and Approximate Numerical Expressions must be translated into meaningful quantities, coherent with the physics of the problem. What do “faster”, “a bit higher”, “little to the right”, and “more assistance” mean?
The second objective is to design new multimodal models fusing VLM/LLMs and multimodal pipelines to predict the human’s intent and minimize the need for corrections. Natural language instructions may be incomplete or unclear, but cameras and microphones (or other sensors) could provide sufficient contextual information to generate an appropriate motion. For example, “take that” could be easily translated into “grasp the bottle”, if it is the only item in front of the robot. “Move a bit to the right” needs clarifications, but also estimation of physical quantities that are context dependent.
The third objective is to detect emergency commands, leveraging both LLMs and audio processing models for nonverbal communication, and generating suitable robot’s reactive behaviors. Humans are often unable to speak clearly when they interact with a robot: sometimes, fear takes over and they do not speak at all, or they mumble, or scream, when they could just say a clear “stop”. Detecting emergency commands is critical to be able to deploy the robots into the real world. For example, “Watch out”, “Attention!” are difficult to translate into precise motions, and require one-shot evaluations because of the urgent nature of the command.
Deadline : 2026-04-18
About The National Institute for Research in Computer Science and Automation (Inria), France –Official Website
The National Institute for Research in Computer Science and Automation (Inria) is a French national research institution focusing on computer science and applied mathematics. It was created under the name Institut de recherche en informatique et en automatique (IRIA) in 1967 at Rocquencourt near Paris, part of Plan Calcul. Its first site was the historical premises of SHAPE (central command of NATO military forces), which is still used as Inria’s main headquarters. In 1980, IRIA became INRIA. Since 2011, it has been styled Inria.
Inria is a Public Scientific and Technical Research Establishment (EPST) under the double supervision of the French Ministry of National Education, Advanced Instruction and Research and the Ministry of Economy, Finance and Industry.
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