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: 2022-04675 – PhD Position F/M Markerless 3D localization of surgical tool in a per-operative context
The objective of this thesis is to develop robust methods for detection, localization and tracking of objects (without markers) in RGB-D image sequences. Using deep neural network-based approaches, we aim to detect, classify and initialize a pose computation process for surgical instruments present in the images (eg, [Rad 2017]). Then, model-based tracking and localization approaches using both contours and depth maps provided by the RGB-D camera will be proposed [Marchand, 2016, Trinh, 2018]. The complexity of the surgical instruments under consideration requires the development of GPU (Graphics Processing Unit) based approaches to ensure a fast and complete projection of the model into the images [Petit 2014]. As the camera is itself mobile, the position of the objects in a fixed reference frame (in which the anatomical landmarks will also be expressed) requires the localization of the camera w.r.t. the environment that will be done using Visual Inertial SLAM methods assisted by an IMU (Inertial Measurement Unit). Moreover, to deal with fast movements, the prediction of object position, integrating inertial data, will be managed thanks to particle filters on SE(3). To validate the system, an estimation of the measurement error will be performed by an external system giving the ground truth (either by mounting the camera on a robot or by using a Vicon 3D measurement system).
Deadline : 2023-06-30
(02) PhD Degree – Fully Funded
PhD position summary/title: 2022-05011 – PhD Position F/M Trustworthy multi-site privacy-preserving technologies
While AI techniques are becoming ever more powerful, there is a growing concern about potential risks and abuses. As a result, there has been an increasing interest in research directions such as privacy-preserving machine learning, explainable machine learning, fairness and data protection legislation.
Deadline : 2023-05-31
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(03) PhD Degree – Fully Funded
PhD position summary/title: 2022-05379 – PhD Position F/M PhD student on federate learning and multi-party computation techniques for prostate cancer
The goal of the multi-disciplinary FLUTE project is to advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments.
Deadline : 2023-05-31
(04) PhD Degree – Fully Funded
PhD position summary/title: 2022-05169 – PhD Position F/M Modeling Kinesthetic and Tactile Properties of Virtual Environments
The goal of this PhD is to propose a complete haptics pipeline that is agnostic on the haptic device used and integrate not only the definition of the haptic properties, but also the interaction characteristics of the user. The rendering of the haptic properties should not only be driven by the virtual object properties, but also by the actions of the user in the virtual environment [Vizcay 2021]. Thus, the interaction capabilities of the user should be taken into account [Dewez 2021]. In particular, avatars, the users’ virtual representation, is becoming ubiquitous in virtual reality applications. In this context, the avatar becomes the medium which enables users to interact with the virtual environment and also the main source of tactile and kinesthetic sensations. Moreover, the user’s avatar can modulate the perceived haptic sensations [Jauregui 2014], enabling a wider range of sensations that the ones provided solely by haptic actuators. Finally, haptic sensations should be congruent with the actual user’s actions in order to avoid the potential generation of an uncanny valley [Berger 2018], which is strongly linked with the notions of presence [Skarbez 2017] and virtual embodiment [Kilteny 2012].
Deadline : 2023-05-31
(05) PhD Degree – Fully Funded
PhD position summary/title: 2023-05752 – PhD Position F/M [ALRC 2023] Mathematical and numerical analysis of dissipative problems with free boundaries
The goal of this thesis is the mathematical study (well-posedness, asymptotic behavior) of a simplified model in order to build a better understanding of a more complex model recently developed by our team. Numerical simulations of the simplified model are also expected. The mid-term goal is to transpose the obtained results to the full model.
Deadline : 2023-04-18
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(06) PhD Degree – Fully Funded
PhD position summary/title: 2023-05753 – PhD Position F/M Neurons, Burst and Adaptive Controller/Observer: Induced Contraction
The development of digital technology is no longer sustainable and the end of `Moore’s law’ is now happening. Neuromorphic engineering is a response to this inefficiency of digital machines in comparison to the animal world, in which the bits and clocks of digital computers are replaced by the spikes and rhythms of analog electronic circuits. We believe that by studying the non-intuitive analogy that exists between neurons and classical adaptive controllers: the appearance of bursting with or without injection of white noise, we head for the theory of designing event-based online adaptation, which is energy efficient compared to the digital machines prevalent these days. Moreover, our hypothesis is that bursting is necessary to induce contraction, hence for adaptation, in both examples of neurons and classical adaptive controllers. Thereby, we plan to base our study on the theory of induced contraction, motivated by the phenomenon of noise-induced synchronization of neurons.
Deadline : 2023-04-18
(07) PhD Degree – Fully Funded
PhD position summary/title: 2023-05754 – PhD Position F/M [ALRC 2023] Synchronization of uncertain and different-type systems via reinforcement learning
The problem of synchronizing different and uncertain dynamical systems (agents) appears in multiple contexts: electrical, mechanical or biological systems, network communications, etc. The difficulty comes from the need to force distinct dynamics (e.g., of mass, size, or even physical nature) to make similar movements by rejecting external disturbances. The uncertainty of the models for the agents introduces another technical obstacle for the realization of synchronous movements. The connection topology (leader-follower, or mutual), and the associated communication delays, represent other complications to synchronization, which must also be neglected by agents. Finally, constraints on allowable movements for each participant (e.g., avoiding collisions) turn this problem into a highly nonlinear and nonconvex question, where conventional control theory fails to provide a solution. The existing methods, mainly related to the model predictive control approach, deal with simple linear models of the systems, with a limited uncertainty, while assuming a convexity of the optimized goal functional, which is rarely observed in practice. The objective of this thesis project is to design synchronization control algorithms under severe uncertainty conditions, with the presence of other uncertain agents, which penalize the risk of error and its cost. The complexity of the posed problem requires an interdisciplinary approach for its solution, as envisaged in this project, which belongs to an intersection of the fields of machine learning and control theory. It is intended to combine reinforcement learning and model predictive control approaches for the realization of synchronization strategies for different and uncertain dynamic agents.
Deadline : 2023-04-18
(08) PhD Degree – Fully Funded
PhD position summary/title: 2023-05757 – PhD Position F/M [ALRC 2023] Interaction beyond the sensorimotor loop
The Loki research team (Inria Center of the Université de Lille, France) is looking for a Ph.D. candidate to start a thesis in October 2023. The Ph.D. thesis will focus on the study of the limits of human perception and action (the sensorimotor loop) in unusual interactive environments, and on the consequences of these limits on the design of interactive devices. A master research internship (April/May to August/September 2023) is possible and strongly encouraged.
Deadline : 2023-04-18
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(09) PhD Degree – Fully Funded
PhD position summary/title: 2023-05756 – PhD Position F/M Using haptic cues to improve micro-gesture interaction
As part of the ANR MIC project, the Ph.D. candidate will work on interaction techniques with micro-gestures. Micro-gestures are hand gestures performed on one hand with the same hand. Examples include tap and swipe gestures performed by one finger on another finger. The Ph.D. thesis aims at improving micro-gesture interaction by leveraging haptic feedback and guidance. The research goal is to design, implement and evaluate interaction techniques based on the innovative combination of micro-gesture and haptic cues for different tasks, starting with pointing and command selection. Later, the candidate will work on specific tasks in an AR/VR environment, emerging from the developed applications in the MIC project, possibly the positioning, rotation, and scaling of 3D objects, as well as text entry. These interaction techniques leverage the sensorimotor loop to foster fast, incremental, reversible, and discoverable interaction.
Deadline : 2023-04-18
(10) PhD Degree – Fully Funded
PhD position summary/title: 2023-05783 – PhD Position F/M Object Detection from Few Multispectral Examples
The project aims at providing deep learning-based methods to detect objects in outdoor environments using multispectral data in a low supervision context, e.g., learning from few examples to detect scarcely-observed objects. The data consist of RGB and IR (Infra-red) images which are frames from calibrated and aligned multispectral videos.
Deadline : 2023-04-12
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(11) PhD Degree – Fully Funded
PhD position summary/title: 2023-05787 – PhD Position F/M Privacy-preserving decentralized learning through Model fragmentation and Private Aggregation
Unfortunately, recent works have shown that, in spite of their promises, both of these approaches can be subject to privacy attacks, such as membership inference, data reconstruction, or attribute inference, that make it possible for malicious participants to access private and or sensitive information through the learning process. This PhD aims to improve the privacy protection granted by decentralized learning by exploring how model fragmentation, a technique developed by the WIDE team within the ANR Pamela project (2016-2020), can be combined with private aggregation and random peer sampling, two of the strategies successfully applied to P2P networks.
Deadline : 2023-04-09
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(12) PhD Degree – Fully Funded
PhD position summary/title: 2023-05768 – PhD Position F/M Balancing Performance and Sustainability for FaaS in the Fog
This thesis will explore QoS-driven, energy-aware management of FaaS applications in fog environments. Specifically, the goal is to develop an automated management solution that can ensure QoS requirements for FaaS applications while also reducing energy and carbon usage. This solution will have the ability to evaluate the potential costs and benefits of different management actions [6] by predicting their impact on performance and energy consumption [7]. To achieve this, the solution will use performance interference analysis techniques that were initially developed for High Performance Computing (HPC) applications [8,9] and adapt them to the specific characteristics of FaaS workloads [4]. Energy and carbon will be considered as first-class resources, and management will be guided by QoS requirements as well as energy consumption requirements [10], formalized in Service Level Agreements (SLAs).
Deadline : 2023-04-08
(13) PhD Degree – Fully Funded
PhD position summary/title: 2023-05764 – PhD Position F/M Towards an efficient economic orchestration of 5G and beyond networks
The goal of the thesis is to identify, to formally define the economic relations between partners, and to embed these aspects in the service offering and provisioning and their associated operation and management. The purpose is to investigate the potential need to regulate the relations towards an efficient orchestration, and compliance to existing legislation, particularly the one around network neutrality: is a technological proposition an infringement to the current legislation, and can it be adapted?
Deadline : 2023-04-08
(14) PhD Degree – Fully Funded
PhD position summary/title: 2022-05436 – PhD Position F/M Creative Visualization Sketching
Unfortunately, dominant visualization systems target data-exploration and data-analysis tasks and fail to meet communication purposes [Kosara, 2016]. Previous studies [Bigelow, 2014] also suggest that current visualization tools impose a data-to-graphics workflow that hinders visual thinking. As a result, the process of creating an original infographic can be extremely manual, involving multiple tools that are largely disconnected from the underlying data. In contrast, we aim to address the more ambitious goal of computer-aided design that treats infographic creation as a visual-thinking process [Ware, 2008]. This process is driven by the graphics, starting from sketches, moving to flexible graphical structures that embed constraints, and ending with data and generative parametric instructions, which can then re-feed the designer’s sketches and graphics.
Deadline : 2023-04-01
(15) PhD Degree – Fully Funded
PhD position summary/title: 2022-04906 – PhD Position F/M Socially-Aware Embodied Conversational Agents: Achieving Task and Social Goals in Human-Computer Conversation with students
For more information on the project, potential candidates should look at the SARA (Socially-Aware Robot Assistant) website and RAPT (Rapport-Aligned Peer Tutor) projects at <http://articulab.hcii.cs.cmu.edu/projects/> and read some of the publications associated with the project, here <http://articulab.hcii.cs.cmu.edu/publications/>
Deadline : 2023-03-31
(16) PhD Degree – Fully Funded
PhD position summary/title: 2022-05622 – PhD Position F/M Integrated localization and mapping for autonomous vehicles
The person recruited is responsible for carrying out research and development in the field of mobile vehicle localization and environment mapping using on-board sensors. (S)he will contribute to the promotion of research work by publishing the work in conferences, journals and scientific journals but also by contributing to patents. (S)he will support his colleagues in demonstration and dissemination activities. Finally, (s)he will contribute to the drafting of reports, articles and documentation for scientific and reporting purposes. Finally, (s)he will be required to work on the Valeo (Créteil) and Inria (Paris and Rocquencourt) sites.
Deadline : 2023-03-31
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(17) PhD Degree – Fully Funded
PhD position summary/title: 2023-05743 – PhD Position F/M In-natura data processing systems for environmental observation under energy constraints
Objective of the thesis: The objective of this thesis is to design and adapt the Fog-computing system developed by IRISA to meet the specific needs of environmental observation. The proposed solutions will be tested and qualified in-natura on the “green cross,” an area of the Beaulieu campus which has been renatured. This close common space, at the interface between laboratory and research observatory, aims to promote a vision of interdisciplinarity through experimentation, and offers a context conducive to the involvement of students during practical work. It will strengthen interactions between the units of 2 major research labs of the university (environmental systems, science and information technology).
Deadline : 2023-03-31
(18) PhD Degree – Fully Funded
PhD position summary/title: 2022-05148 – PhD Position F/M Online Federated Learning with Non-i.i.d. Data
The focus of the FedMalin project is on federated learning, where a number of clients with data communicate with a server in order to estimate a statistical model; e.g., a regression model based on deep neural networks. Federated learning systems support model estimation without providing the data stored in the clients directly to the server. Instead, the clients estimate local models and exchange the local model parameters or associated (sub)gradients with the server. This can dramatically reduce the amount of communication required and provide increased privacy.
Deadline : 2023-03-31
(19) PhD Degree – Fully Funded
PhD position summary/title: 2023-05784 – PhD Position F/M IoT based decentralized identity checking
IN Groupe has been a partner of the French State for nearly 500 years and offers state-of-the-art identity solutions and secure digital services, integrating electronics, optics and biometrics. From components to services, including documents and interoperable systems, as a global specialist in identity and secure digital services, IN Groupe is always on hand to make life easier for everyone. Whether assisting nations in exercising their sovereignty, or protecting the identity of citizens, or preserving companies’ confidentiality, IN Groupe, as an organization with digital sovereignty, helps to ensure that everyone has a fundamental right: the Right to Be You. IN Groupe is the new company name of “Groupe Imprimerie Nationale”.
Deadline : 2023-03-31
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(20) PhD Degree – Fully Funded
PhD position summary/title: 2023-05744 – PhD Position F/M Optimization of resources placement in Fog-based IoT systems based on latency analysis
In the context of IoT applications (i.e. critical response time environments such as Smart City sensing or Vehicular networks) latency is at the center of a tremendous number of studies to optimize the placement of resources in distributed architectures. To ensure that the quality of service is guaranteed, several solutions exist to reconfigure the components placement (migration) and can reduce the overall latency by changing the components and routes. However, knowing precisely which component is the source of the problematical latency remains scarcely addressed. When taking a decision for a reconfiguration or a migration, which can be triggered due to latency issue, it can be beneficial to check if the source of the latency can be solved before instantiating a migration or a full reconfiguration. Some studies exist where a comparison of response time is done between the major cloud actors depending on the load [1, 6]. Proper measurement protocols exist but always refer to specific case studies [2] and would not allow to be integrated in fog systems.
Deadline : 2023-03-30
(21) PhD Degree – Fully Funded
PhD position summary/title: 2022-05223 – PhD Position F/M Reliable and cost-efficient data placement and repair in P2P storage over immutable data
Deadline : 2023-03-16
(22) PhD Degree – Fully Funded
PhD position summary/title: 2023-05790 – PhD Position F/M Acoustic to Articulatory Inversion by using dynamic MRI images
The first objective is the inversion of the acoustic signal to recover the temporal evolution of the medio-sagittal slice. Indeed, dynamic MRI provides two-dimensional images in the medio-sagittal plane at 50Hz of very good quality and the speech signal acquired with an optical microphone can be very efficiently deconstructed with the algorithms developed in the MultiSpeech team (examples available on https://artspeech.loria.fr/resources/). We plan to use corpora already acquired or in the process of being acquired. These corpora represent a very large volume of data (several hundreds of thousands of images) and it is therefore necessary to preprocess them in order to identify the contours of the articulators involved in speech production (mandible, tongue, lips, velum, larynx, epiglottis). Last year we developed an approach for tracking the contours of articulators in MRI images that gives very good results [10]. Each articulator is tracked independently of the others in order to keep the possibility to analyze the individual behavior of an articulator, e.g. in case one of them fails. The automatically tracked contours can therefore be used to train the inversion.
Deadline : 2023-03-16
(23) PhD Degree – Fully Funded
PhD position summary/title: 2023-05772 – PhD Position F/M Spatial data fusion and scaling for small water reservoir monitoring at regional scale
The water reservoirs smaller than 1.5 ha represent up to 60 % of surface water bodies [France : INPE-MTES] being just a small fraction of the total surface water bodies volume. However these small reservoirs disseminated all over surfaces are of great importance for a smart local use of water (cf. Varennes de l’eau agricole), to preserve ecosystems and to mitigate hydroclimatic risks. Water reservoir monitoring from space is effective or planned (e.g. SWOT) for large reservoirs. However, these missions are not adapted for small water reservoirs since their detection, the monitoring of their water volumes and storage capacities require both very high spatial resolution and high revisiting frequency. Monitoring the water stocks of small reservoirs along seasons is hence still dramatically missing at the global scale. The detection of such small reservoirs is not an issue since their contours can exist in geodatabases (e.g. BD-Topage-France) or it can be processed using a small number of spatially highly resoluted images (Pleiades, Pleiades Neo). Nevertheless, the characterization of their individual geometry and the monitoring of the waterstock is still a challenge.
Deadline : 2023-03-14
(24) PhD Degree – Fully Funded
PhD position summary/title: 2023-05762 – PhD Position F/M Generalization bounds for neural networks
Understanding generalization in deep learning has been one of the major challenges in statistical learning theory over the last decade. While recent work has illustrated that the dataset and the training algorithm must be taken into account in order to obtain meaningful generalization bounds, it is still theoretically not clear which properties of the data and the algorithm determine the generalization performance. In this phd, we will approach this problem from a dynamical systems theory perspective and exploit the fractal structure that the stochastic optimization algorithms produce [1].
Deadline : 2023-03-04
(25) PhD Degree – Fully Funded
PhD position summary/title: 2022-05026 – PhD Position F/M Koopman operator modelling of non-linear dynamical systems for ensemble methods.
The candidate will perform connections between classical ensemble methods and the constructed manifold. He will perform numerical simulations in order to demonstrate the ability of the method to perform efficient estimations. Based on codes available in the team, increasing complexity will be possible through a hierarchy of oceanic models (for instance, 2D barotropic quasi-geostrophic (QG), surface QG, multilayer models, rotating shallow water) or incompressible fluid mechanics simulations (2D or 3D mixing layer). The candidate will develop new data assimilation methodologies applied to these flows. In addition to the non-linear learning, the transport of the RKHS along the manifold allows to consider observations that have not been acquired exactly at the at at which the estimation is performed. This confers a large potential impact of the methodology.
Deadline : 2023-03-01
(26) PhD Degree – Fully Funded
PhD position summary/title: 2022-05186 – PhD Position F/M Deep interactive control of virtual character’s motion based on separating identity, motion and style (Inria/InterDigital Ys.ai project)
Character animation has a large set of potential applications, in videogames, movie industry, sports, rehabilitation, ergonomics, training, etc. To capture a 3D human shape in motion, two options are available at the moment: either a direct acquisition of the surface mesh thanks to a calbrated mutli-camera setup, or a skinning technique from the animated skeleton. However, being able to capture and reproduce the expressivity of a human motion is difficult, as it involves several subtle parameters, some of them being lost when modeling human performance as joint angles: angles, contacts on the body surface, velocity profiles, accelerations, distance or coordination between body parts, etc. With the development of robust body shape reconstruction based on cheap sensors, such as RGB cameras or depth sensors, directly manipulating the visible surface of the character has become a very active field of research.
Deadline : 2023-02-28
(27) PhD Degree – Fully Funded
PhD position summary/title: 2022-05275 – PhD Position F/M Self-supervised learning for implicit shape reconstruction
These models are commonly trained using dense points sampled near the ground-truth surface. Hence, training them to perform reconstruction from images or point clouds requires typically substantial full 3D supervision that is hard to acquire. With the prospect of alleviating this expensive data dependence, we will explore in this project the extension of self-supervised methods to 3D implicit reconstruction. Existing self-supervised learning techniques in vision focus mostly on holistic 2D recognition tasks [7,8]. Our goal is to design self-supervised learning mechanisms that can reason locally [9] and benefit from inductive biases in 3D euclidean space.
Deadline : 2023-02-28
(28) PhD Degree – Fully Funded
PhD position summary/title: 2022-05392 – PhD Position F/M Stochastic modelling of communications networks
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.
Deadline : 2023-02-28
(29) PhD Degree – Fully Funded
PhD position summary/title: 2023-05710 – PhD Position F/M Video-based dynamic garment representation and synthesis
It has recently become possible to reconstruct sequences of temporally coherent 3D models of humans in clothing from input videos, which subsequently allows to synthesize new animations, e.g. [1,2]. Such state-of-the-art approaches typically learn a model of clothing on top of a parametric body model and are hence limited to relatively tight clothing. Our prior work allows modeling more diverse clothing using a fuzzy correspondence of the garments and the underlying parametric body, at the cost of losing fine-scale geometric detail in the model [3]. An orthogonal line of works models clothing using garment templates, and learns the garment’s dynamic behavior during deformation of the person wearing the garment e.g. [4]. This strategy allows modeling detailed complex wide and multi-layered garments, and can be used to synthesize realistic dynamic videos [5].
Deadline : 2023-02-28
(30) PhD Degree – Fully Funded
PhD position summary/title: 2023-05735 – PhD Position F/M Designing Multimodal Human-Computer Partnership for Music Practice
Professional musicians are a particularly demanding audience, who seek to generate new compositions or forms of musical expression. However, today’s professional music tools focus primarily on implementing existing ideas, such as rendering notes onto a scale, rather them helping musicians as they generate new ideas. ExSitu’s research focus is on developing interactive tools and systems that support fluid interaction between creative professionals and their tools. We seek to create expressive human-computer partnerships, where the tools help users explore ideas according to their expertise, experience and personal taste, while adapting to changing goals and contexts.
Deadline :2023-02-28
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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