23 Postdoctoral Fellowship at Inria, France

Atlantis is  a joint project-team  between Inria and  the Jean-Alexandre Dieudonné Mathematics Laboratory at  Université Côte d’Azur. The team  gathers applied mathematicians and  computational scientists who are collaboratively undertaking  research activities aiming at the design, analysis, development and  application of advanced numerical methods for solving systems of  partial differential equations (PDEs) modelling nanoscale light-matter interaction problems. In this context, the team is  developing  the   DIOGENeS  [https://diogenes.inria.fr/]  software suite,  which  implements  several Discontinuous  Galerkin  (DG)  type methods tailored to the systems  of time- and frequency-domain Maxwell equations  possibly coupled  to  differential  equations modeling  the behaviour of propagation  media at optical frequencies. DIOGENeS also includes a component dedicated to the optimization of geometrical characteristics of nanostructures driven by some performance objective in the contex of inverse design strategies of nanophotonic setups.   DIOGENeS is a unique  numerical   framework  leveraging   the  capabilities   of  DG techniques  for  the simulation  of  multiscale  problems relevant  to nanophotonics and nanoplasmonics.

 The successful candidate will be responsible for the following tasks:
– Develop a lightweight, calibrated acquisition method that can be easily used by museum and heritage site professionals.
– Develop a representation and rendering method associated with the acquisition acquisition method in order to
– render furniture under different lighting conditions (artificial, outdoor) and from different different points of view
– Animate furniture (e.g., drawers opening)
– Implement the rendering method in an Open-Source protype
– Study the feasibility of integrating the rendering method developed into a commercial such as Unreal Engine.

This project is based on a collaboration between Scool and a medical team at Inserm/CHU de Lille that have been active for 5 years now.

The scientific goal of this collaboration is to investigate the exploitation of data to improve patient follow-up after surgery. We model this problem as a sequential decision making under uncertainty problem.

This collaboration has already produced interesting results, in the form of a website, presentations in conference, and publications in top machine learning conferences, and top medicine journals.

This collaboration involves about 10 people: data engineer, PhD student, post-doc, researchers, professors.

This post-doctoral position will be supported by the  HE Trumpet project, the HE Flute project and/or the PEPR IA Redeem project.      While this position will be in the MAGNET team in Lille, we will collaborate with the several European project partners.

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.
Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data is not revealed. Notions such as (local) differential privacy and its generalizations allow to bound the amount of information revealed.

 The MAGNET team is involved inthe related TRUMPET, FLUTE and REDEEM projects, and is looking for team members who can in close collaboration with other team members and national & international partners contribute to one or more of these projects.  All of these projects aim at researching and prototyping algoirhtms for secure, privacy-preserving federated learning in settings with potentially malicious participants.  The TRUMPET and FLUTE projects focus on applications in the field of oncology, while the REDEEM project has no a priori fixed application domain.

The research project will explore quantum protocols based on the concept of quantum nonlocality and quantum networks (see arXiv:2104.10700). A non-exhaustive list of potential projects is:

• Methods for characterizing quantum correlations beyond the Bell scenario:
  • mathematical foundation of these methods (C* algebras, noncommutative polynomial optimization): see e.g. arXiv:2210.09065, arXiv:2212.11299, arXiv:2301.12513
  • improve/find new algorithms for characterizing these correlations
  • numerical developpement of these algorithms, see e.g. arXiv:2211.04483
• Understanding the foundational implications of quantum correlations in networks, see e.g. arXiv:2101.10873 and arXiv:2105.09381
• Develop the applications of network nonlocality to certification protocols, such as
  • randomness generation: arXiv:2209.09921 
  • self testing of measurements and states: arXiv:1807.04956, arXiv:2201.05032
• Adapt existing protocols for their experimental implementation
• Develop practical benchmarks of the concept of ‘Genuine Multipartite Nonlocality’ introduced in arXiv:2105.09381
• Develop SDP relaxations for condensed matter problems, see e.g. arXiv:2212.03014, arXiv:2310.05844, arXiv:2311.18707, arXiv:2311.18706 
• Explore the limits of quantum distributed computing, see e.g. arXiv:1810.10838, arXiv:0903.113

Any other suggested research project in quantum information theory can be discussed (both from the physics, the computer science or the mathematical viewpoint).

This project will be carried out within the framework of CONCACE, a joint Inria-Industry team involving Airbus, Cerfacs and Inria, focusing on the theme of composability applied to high-performance computing.
For wave propagation simulations in electromagnetics or acoustics, Airbus makes extensive use of H-matrix solvers to solve surface integral formulations. These techniques use a hierarchical partitioning of the physical object under consideration (e.g. an aircraft) to produce a hierarchical partitioning of the (dense) matrix of the linear system. Most of the matrix blocks thus created are numerically low-rank, and can therefore be stored in compressed form, occupying much less memory space. The H-matrix solver enables us to store these blocks, use them for matrix-vector products and factorize the matrices in our problems while keeping them in this storage format. This is a crucial tool for modeling certain aircraft physics.
The development of the h-matrix library is the fruit of a collaboration since 2010 between Airbus and Inria, it is in C++ and uses the task-based programming paradigm with the StarPU execution engine.  The proposed topic will lead to further development of this h-matrice tool, in collaboration with the industrial partner.

The position is in the framework of dAIEDGE—A network of excellence for distributed, trustworthy, efficient and scalable AI at the Edge—funded by the European Union.

The vision of the dAIEDGE Network of Excellence is to strengthen and support the development of the dynamic European edge AI ecosystem under the umbrella of the European AI Lighthouse and to sustain the advanced research and innovation of distributed AI at the edge as essential digital, enabling, and emerging technology in an extensive range of industrial sectors.

This postdoctoral position is part of the 3DS-INRIA MediTwin project, aimed at developing a preoperative surgical planning tool to support medical teams in managing single ventricle patients.

Once the surgical procedure is determined by the 3DS preoperative planning tool, the goal is to provide a simplified digital twin (0D model or other reduced model) that incorporates morphological changes from the planning tool as well as patient-specific functional data (e.g., from pulse wave Doppler ultrasound). This 0D digital twin can be calibrated using either a high-fidelity 3D digital twin or direct patient data, ultimately enabling postoperative outcome prediction and improved therapeutic decision-making.

The postdoctoral researcher will join the multidisciplinary and international Simbiotx Inria research team (https://team.inria.fr/simbiotx/) and will closely collaborate with cardiopediatric teams at 3DS, the Inria Commedia team for high-fidelity models, and the Necker-Enfants Malades Hospital.

The PULSE challenge, shared by Inria and Qarnot computing, aims to develop and promote best practices in geo-distributed hardware and software infrastructures for intensive computing with a reduced environmental footprint.

The aim of this postdoc is to better quantify the difference in environmental footprint of different models of geo-distributed computing, so as to be able to better manage the induced impacts (according to a multi-criteria approach). Ultimately, we hope to be able to recommend best practice hardware and software architectures to drastically reduce impacts while offering the best quality of service to end-users.

In this Postdoc, we want to study active strategies of data acquisition involving a master robot (the ground vehicle) and two slave robots (aerial robots) in order to optimize in terms of accuracy and stability the mapping of the environment. Each robot will be equipped with its own large field vision with a time stamping. We assume also that synchronized velocity control inputs can be sent to the ground and aerial robots. The master robot centralizes the information and manages the multi-robots formation. One robot of the formation, so-called the leader (not necessary the master) is manually driven by a human operator who ensures the completness of the exploration of the scene.

This post-doc will take palce in the  Chroma team in Lyon. The team studies algorithms and models to enable autonomous agents/robots to perceive, decide and adapt to their environment, by combining probabilistic methods, machine learning, planning techniques, multi-agent decision-making and constrained optimisation tools. The team develops and maintains several experimental platforms, and is involved in several academic and industrial projects in the field of mobile and aerial robotics.

This two-year post-doc is part of the INRIA/AID  BioSwarm project: Bio-inspired algorithms for collective research and decision-making in drone swarms. The post-doc will be carried out in collaboration with Olivier Simonin (CITI/Chroma) and Emanuele Natale (Inria Sophia Antipolis), co-PIs of the BioSwarm project.

The objective of this post-doc position is to contribute to the field of Gaussian Avatar reconstruction from few views. The post-doc will work closely with a research engineer hired on the project whose role is to prepare the datasets, help testing and assessing the quality of the results, optimizing the performances and integrating the results in the framework designed by the EMOVA.ME company.

The tasks of the post-doc will consist in:

• working on key qualitative limitations in the current avatar reconstruction approach, and prototyping improvements;
• improving the use of 2D/3D Gaussian Splatting techniques in the avatar reconstruction process and evaluating its qualitative impact, specifically on details such as eyes / ears / hair /beard, through the design of novel perceptual metrics and user studies;
• Improving separation between materiel estimation (albedo, roughness, specularity) and illumination, to enable a re-rendering of the avatar in different CG lighting conditions;
• introducing priors and additional guidance techniques to reduce the number of input views required and regularize the mesh and radiance field estimations;

Given an AI model’s placement in an IDN, we will study inference delivery strategies to be implemented at each node in this task. For example, a simple inference delivery strategy is to provide the inference from the local AI model if this seems to be accurate enough or to forward the input to a more accurate model at a different node if the inference quality  improvement (e.g., in terms of accuracy) compensates for the additional delay or resource consumption. Besides this serve-locally-or-forward policy, we will investigate more complex inference delivery strategies, which may allow inferences from models at different clients to be combined. To this purpose, we will rely on ensemble learning approaches [MS22] like bagging [Bre96] or boosting [Sch99], adapting them to IDN distinct characteristics. For example, in an IDN, models may or may not be trained jointly, may be trained on different datasets, and have different architectures, ruling out some ensemble learning techniques. Moreover, queries to remote models incur a cost, which leads to prefer ensemble learning techniques that do not require joint evaluation of all available models.

The appointment is in the broad intersection of information theory, communication theory, and game theory. As part of the national programme France 2030, funded by the French Government, the objective of this position is to study the fundamental limits on the reliability and throughput of decentralized communications systems. The underlying assumption is that independent receivers might broadcast a signal that can be observed by all receivers and thus, used to enhance reliability, throughput, or a convex combination of both. More importantly, transmitters are assumed to use a final constellation and a limited number of channel uses (finite block-length regime). 

The research activity is taken over within a strong national cooperation formed around the larger project, namely the PEPR — Future Networks. The postdoc will participate in national workshops and seminars which are regularly organized within this larger project. 

 

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Post-Doctoral Research Visit F/M Characterisation of quantum computational systems

Deadline : 2025-03-31

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 This postdoctoral research will develop new algorithms that use control theory methods to enhance the intelligibility of the distorted voice when using diving equipment. It can be described in specific missions designed to achieve this goal. These missions include filtering noise associated with breathing, addressing the deformation of bilabial consonants, examining the pressure and density of inhaled gases, and understanding how depth affects voice frequency during emission.

Deep Neural Networks (DNN) and hardware accelerators are both leading forces for the recent progress in Edge AI. On the one hand, a new neural architectural paradigm is proposed each month, striving for more accuracy and efficiency. On the other hand, the hardware market has shifted towards designing devices that ensure both flexibility and generality for less energy demands while satisfying the user experience with less latency.
When DNN models are implemented on resource-constrained systems (e.g., edge computing), it becomes inevitable to meticulously optimize them to strike the optimal balance between accuracy, execution latency and energy efficiency. In order to address this particular difficulty, our objective in this project is to tackle Hardware-aware Neural Architecture Search (HW-aware NAS) as a new AutoML paradigm, targeting edge systems. HW-aware NAS incorporates hardware efficiency as an additional optimization objective during the neural architecture design space exploration.