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 Fast solvers for studying light absorption by nanostructured imagers
The exploitation of nanostructuring in order to improve the performance of CMOS imagers based on microlens grids is a very promising avenue. In this perspective, numerical modeling is a key component to accurately characterize and optimize the absorption properties of these complex imaging structures which are intrinsically multiscale (from the micrometer scale of the lenses to the nanometric characteristics of the nanostructured material layers). The present PhD project is proposed in the context of a collaboration between the Atlantis project-team of Inria research center at Université Côte d’Azur and STMicrolectronics (CMOS Imagers division of the Technology for Optical Sensors department) in Crolles. A Cifre funding will support this project. The objectives of the project are to design (1) a fast electromagnetic simulation approach based on a model reduction technique, to characterize numerically the light trapping in digital imagers exploiting nanostructured pixels and, (2) a multi-objective optimization strategy of the geometrical characteristics of the nanostructuring in order to simultaneously maximize the light absorption in a pixel and to minimize the crosstalk phenomenon between neighboring pixels. For the first time, in addition to the rigorous methods for solving Maxwell’s equations, we will be able to benefit from an alternative simulation approach based on model reduction. This new approach can be used in an optimization process and the expected gain in total computation time would be between 10 to 1000.
Deadline : 2024-06-30
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Designing highly efficient ultrafast dynamical metasurface for LIDAR applications
The present postdoctoral project is part of a collaborative project between the Atlantis project-team from the Inria Research Center at Université Côte d’Azur, (2) CRHEA in Sophia Antipolis, France, and (3) LAAS in Toulouse 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 innovative numerical methods for 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 is a unique numerical framework leveraging the capabilities of DG techniques for the simulation of multiscale problems relevant to nanophotonics and nanoplasmonics.
Deadline : 2024-05-31
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Monitoring Plane for mobile cellular networks
The overall objective of the DIANA project-team is to design, implement and evaluate advanced networking architectures. To do so, the team works to provide service transparency and programmable network deployments in the context of both wired and next generation wireless cellular networks. The team’s methodology includes advanced measurement techniques, design and implementation of architectural solutions, and their validation in adequate experimental facilities. The DIANA team designed, deployed and operates R2lab, a wireless testbed designed with reproducibility as its central characteristics. The team collaborates with Eurecom to deploy and operate an open programmable platform to test post-5G services. Recently, the team enriched R2lab with 5G professional radio units and compute resources managed by Kubernetes clusters to provide an experimental cloud-native environment to test with open source (OAI, SrsLTE) software and some commercially licensed software (e.g. Amarisoft) for 5G/6G networks supporting for example scenarios with disaggregated 5G networks elements. Other recent contributions of the team include: Enhanced Transport-Layer Mechanisms for Multi-Access Edge Computing-Assisted Cellular Networks, Bencharmking Mobile Networks from the Viewpoint of Video Streaming QoE, Introducing Fidelity in Network Emulation, and Enhanced Ray Tracing Techniques for Accurate Estimation of Signal Power.
Deadline : 2023-12-31
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Experimental evaluation of sliced cellular networks
The overall objective of the DIANA project-team is to design, implement and evaluate advanced networking architectures. To do so, the team works to provide service transparency and programmable network deployments in the context of both wired and next generation wireless cellular networks. The team’s methodology includes advanced measurement techniques, design and implementation of architectural solutions, and their validation in adequate experimental facilities. The DIANA team designed, deployed and operates R2lab, a wireless testbed designed with reproducibility as its central characteristics. The team collaborates with Eurecom to deploy and operate an open programmable platform to test post-5G services. Recently, the team enriched R2lab with 5G professional radio units and compute resources managed by Kubernetes clusters to provide an experimental cloud-native environment to test with open source (OAI, SrsLTE) software and some commercially licensed software (e.g. Amarisoft) for 5G/6G networks supporting for example scenarios with disaggregated 5G networks elements. Other recent contributions of the team include: Enhanced Transport-Layer Mechanisms for Multi-Access Edge Computing-Assisted Cellular Networks, Bencharmking Mobile Networks from the Viewpoint of Video Streaming QoE, Introducing Fidelity in Network Emulation, and Enhanced Ray Tracing Techniques for Accurate Estimation of Signal Power.
Deadline : 2023-12-31
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Towards more solid basis for symmetric cryptography
During this PhD we will work on generalizing and improving the existing cryptanalysis families on symmetric cryptography.
Deadline : 2023-12-31
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Grammar-guided multigrid solver optimization for the Stokes equations
The solution of large sparse linear systems is one of the core topics in numerical linear algebra and an active research area in academia and industry. For new and existing applications, the solver usually has to be developed with care, including for example preconditioning techniques. The goal nowadays is to reach large-scale computations. One candidate of solution algorithms to reach this goal are multigrid methods. However, an exponential number of parameter combinations (number of levels, relaxation steps, V or W cycle, …) are possible. Trial and error to find a first idea for an adequate solver becomes thus tedious or even unfeasible. In general, deep domain knowledge is required to find a performant solver for a given problem. But how would it be if an automatic optimization of a solver was possible, that could be used as a first step and then further adapted by the user? This project aims to develop concepts and a toolchain based on artificial intelligence to find the ‘best’ preconditioner for a Krylov subspace iterative solver for the solution of the linear system coming from a discretized Stokes problem. We will focus in particular on a geometric multigrid preconditioner for the (1,1)-block, here, the Laplace operator, to ensure the scalability of the solver when passing to large-scale problems. We will extend the work by J. Schmitt et al. (Constructing Efficient Multigrid Solvers with Genetic Programming, Gecco ’20, 2020), in which a grammar-guided approach for the optimization of multigrid solvers for matrices from a finite difference discretization of the Laplacian operator is formulated. The particular difficulty for the extension to saddle point systems is the large number of possible combinations of solution techniques. These include, for example, preconditioned Krylov subspace solvers that make use of the block structure of the system, Schur complement approaches, deflation and augmentation techniques, or also monolithic multigrid solvers on the whole block system.
Deadline :2023-12-31
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Contention-Aware Scheduling of Storage Resources on Exascale Systems
This thesis is placed in the context of the PEPR NumPEx (https://numpex.fr/), whose goal is to co-design the exascale software stack and prepare applications for the exascale era. This thesis will be co-supervised by Inria and CEA, respectively the Inria center at the University of Rennes and the CEA center at Bruyères-Le-Châtel, near Paris. Beyond the supervision, collaborations within the PEPR with the different laboratories of the consortium are to be expected.
Deadline : 2023-12-16
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Hardware-guided compression & fine-tuning of Transformer-based models
The Inria Rennes – Bretagne Atlantique Centre is one of Inria’s eight centres and has more than thirty research teams. The Inria Center is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Deadline : 2023-12-03
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Topology-aware load balancing for ocean simulation on heterogeneous platforms.
CROCO (Coastal and Regional Ocean Community) is an oceanic modeling system (https://www.croco-ocean.org). An important objective for CROCO is to resolve very fine scales (especially in the coastal area), and their interactions with larger scales. It includes new capabilities such as a non-hydrostatic solver, ocean-wave-atmosphere coupling, evolving sediment dynamics and marine biogeochemistry, and new high-order numerical schemes for advection and mixing. Various HPC improvements of the CROCO model itself are currently carried out with respect to a sustainable support of GPUs and different parallel programming models. Indeed, the current trend in high-performance computing architectures is going even more towards increasing heterogeneity. This is omnipresent on the intra-node computation with accelerator cards as well as on the inter-node level with different hardware and communication behaviors.
Deadline : 2023-11-01
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Learning bimanual robot skills from human demonstrations and natural language
The team LARSEN is involved in the European project euROBIN. One of the main goals of the project is to advance cognition-enabled transferable embodied AI. Scientifically, it will substantially advance four core scientific topics: InterAct, Learning transfer, Transferable knowledge, and Human-center transfer. Three robotics domains are investigated: manufacturing, outdoor and personal robotics. In this project, Inria is leading the personal robotics challenge. In this project, INRIA is leading the personal Robotics Challenge, where bimanual manipulators and humanoid robots must execute a variety of complex manipulation, navigation and interaction tasks in a household scenario. Some of these tasks involve unloading a dishwasher, opening a fridge to take an object, folding clothes, carrying and handing objects to humans. Annual hackathons are organized to foster collaborations among European teams on robotics challenges.
Deadline : 2023-10-31
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Hair Capture and Modeling
The Ph.D. position is part of a joint laboratory between Interdigital, a leading technology and research company, and Inria, the French national institute of computer science and automation. The PhD will be one of several PhD topics around the avatar representation of people, within a collaboration framework between Inria and InterDigital called Nemo.ai. The PhD will start as soon as possible and will last 3 years. It will be supervised by The Morpheo team at INRIA Grenoble Rhône-Alpes and the MetaVideo group at InterDigital Research and Innovation labs in Rennes. The focus of Morpheo’s research is on perceiving and interpreting moving shapes, with applications to character animation, and immersive and interactive environments. The MetaVideo group that will co-supervise the PhD develops representations for the transmission of digital human and avatar character models in interactive environments.
Deadline : 2023-10-07
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(12) PhD Degree – Fully Funded
PhD position summary/title: 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.
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 overall goal of the TRUMPET project is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross-border European datasets with privacy guarantees that exceed the requirements of GDPR. INRIA’s MAGNET team will contribute among others to tasks involving AI algorithms and architectures, federated Learning, privacy platforms, privacy measurement and metrics, privacy-enhancing technologies and applied cryptography
Deadline : 2023-09-30
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M PhD student on federate learning and multi-party computation techniques for prostate cancer
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 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 : 2023-09-30
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Alternative approaches for privacy-preserving in federated learning
Federated learning (FL) enables a large number of IoT devices (mobiles, sensors) to cooperatively to learn a global machine learning model while keeping the devices’ data locally [MMR+17, LSTS20]. For example, Google has applied FL in their application Gboard to predict the next word the users would type on their smartphones [HRM+18]. FL can help to mitigate privacy concerns, as the raw data is kept locally by the users and never needs to be sent elsewhere. However, maintaining the data locally does not provide itself formal privacy guarantees. Many attacks have shown the vulnerability of federated learning systems: the adversary can reconstruct private data points (e.g., images and private features) [ZLH19, GBDM20, DXN+22], infer the membership of the data instance [MSDCS19, ZXN21] and reconstruct the local model of the user [XN21] just by eavesdropping the exchanged messages. As a result, differentially private (DP) algorithms [MRTZ18, BGTT18] have been proposed for FL to protect privacy by injecting random noise into the transmitted messages. DP ensures that if the user changes one training sample, the adversary does not observe much difference in the exchanged messages and then may not confidently draw any conclusions about the presence or absence of a specific data sample. Therefore, attacks are less efficient [JE19]. However, the noise typically deteriorates the performance of the model.
Deadline :2023-09-30
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Ensuring Availability of Internet-connected Constrained Wireless Networks
The PhD position in the scope of the PEPR 5G project HiSec. The HiSec project focuses on cyber-security issues in future networks. These networks have played a key role in service delivery for digital infrastructures. These new networking technologies have also penetrated essential and critical services for our daily lives, such as energy, transportation or healthcare.
Deadline :2023-09-30
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Self-supervised learning for implicit shape reconstruction
The Inria Rennes – Bretagne Atlantique Centre is one of Inria’s eight centres and has more than thirty research teams. The Inria Center is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc
Deadline :2023-09-30
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(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Verified Offloading Orchestration of Network Functions at the Edge
This work is in the context of the HiSec project. The HiSec project is part of the 5G PEPR founded by the ANR, which focuses on cyber-security issues in future networks. These networks have played a key role in service delivery for digital infrastructures. These new networking technologies have also penetrated essential and critical services for our daily lives, such as energy, transportation or healthcare. The pervasive use of digital services and networks to control these critical infrastructures significantly increases the attack surface and the opportunities for attackers. We regularly observe attacks against these infrastructures, leading to successful compromise and very significant impacts. The objective of the HiSec project is thus to handle cybersecurity issues in these environments, and propose new mechanisms to protect these networks and detect attacks, attacks against the networking infrastructure itself, or against the services hosted or the users of the network.
Deadline : 2023-09-30
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Energy efficient data management: Data reduction and protection meet performance and energy
The amount of data observed from the world is growing exponentially, reaching 64.2 zettabytes in 2020. To meet the continuously growing demand for computing resources to store and process Big Data, large cloud providers have equipped their infrastructures with millions of energy hungry servers distributed on multiple physically separate data-centers. This results in a tremendous increase in the energy consumed to operate these data-centers. However, as the data and the scale of data-centers are on the rise, energy consumption will continue to be a major concern in the Cloud. Thus, it is important to make data management in the cloud energy-efficient.
Deadline : 2023-09-30
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Distributed Training of Heterogeneous Architectures
This PhD thesis is in the framework of the Inria-Nokia Bell Labs research initiative on Federated Learning for Cellular Networks and in closer relation with Inria research initiative on Federated Learning, FedMalin https://project.inria.fr/fedmalin/. The PhD candidate will join Nokia Bell Labs research team in Massy, France https://www.bell-labs.com/about/locations/paris-saclay-france/ and will also be a member of the Inria project-team NEO https://team.inria.fr/neo/. The Machine Learning & Systems team, part of the AI Research Lab at Nokia Bell Labs, is composed of computer scientists and data engineers who develop AI-based systems and algorithms bridging the gap between the promise of limitless capabilities of AI and the constraints imposed by real computing and communication systems. NEO is positioned at the intersection of Operations Research and Network Science. By using the tools of Stochastic Operations Research, the team members model situations arising in several application domains, involving networking in one way or the other.
Deadline : 2023-09-30
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Building and analysis of agent-based models for tissue morphogenesis and maintenance coupling energy exchanges and mechanics
This post-doctoral position is in the frame of the ANR project ENERGENCE headed by D. Peurichard (Inria Paris, MAMBA team) and our biological partners at RESTORE, Toulouse. The long lasting collaboration between the two partners led to the development of mathematical models for adipose tissue development and morphogenesis [Peurichard et al, JTB, 2017], and was extended to study AT reconstruction abilities after injury [Peurichard et al, JTB, 2019]. In both studies, it proved to be an invaluable tool to highlight the key role of mechanical interactions in AT. However, the models did not take into account energy exchanges, as metabolism was prescribed by some growth laws not linked to external energy arrivals. In this project, we propose to take a step further and to implement a coupling between energy and mechanics into this framework. The goal of this post-doctoral position is to develop and analyze agent-based models (ABM) to explore and determine the complex feedback loops between energy intakes and local growth laws, and study how the tissue architecture evolves as a result of changes in energy fluxes, modelling cafeteria diet and food deprivation. The parameters of the constructed ABM will be calibrated on experimental measurements at RESTORE and the model resutls will be systematically confronted to experimental data from the litterature and generated by our biological partners.
Deadline : 2023-09-30
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Deep learning techniques for radio identification
With the popularisation of software defined radios (SDRs), an malevolent actor can deploy radio systems for interfering with legitimate communications, communicating on unlicensed bands and listening to private communications. In this proposed PhD work we target in a first moment spectrum sensing capabilities, that can be used to automatically locate and classify opponent transmissions, characterising it in terms of center frequency, occupied bandwidth, activity pattern, modulation and coding schemes, frame structure and more. Then we will study the identification problem, trying to uniquely single out individual transmitters among all transmitters. The proposed work will (i) create good datasets to train and test systems for spectrum sensing and (ii) develop deep learning (DL) systems for spectrum sensing/classification and identification. Nowadays, spectrum surveillance is mainly done with relatively simple systems that require intense human intervention. However, as radio communications systems grow more and more complex in nature and can span larger portions of the spectrum, relying on human-based surveillance risks missing out on improper use of the spectrum. Sophisticated means to detect these transmissions, identify them and locate their source is thus necessary, but remains a complicated task to accomplish.
Deadline : 2023-09-30
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Unsupervised Machine Learning for Wireless Communications
Wireless communication systems involve collecting large amounts of data related to electromagnetic propagation, which is normally used for the purpose of data transmission and demodulation, and then immediately discarded. While it is clear that leveraging the statistical aspects of propagation information (e.g. through learning the characteristics of its distribution and applying appropriate statistical techniques) has the potential to greatly enhance the performance and range of services offered by the network, this approach faces the practical challenges of real-time processing such as a limited computing and storage resources.
Deadline : 2023-09-30
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Topology Design for Decentralized Federated Learning
The Inria centre at Université Côte d’Azur includes 37 research teams and 8 support services. The centre’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d’Azur, CNRS, INRAE, INSERM …), but also with the regiona economic players. With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d’Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
Deadline : 2023-09-30
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Socially-Aware Embodied Conversational Agents: Achieving Task and Social Goals in Human-Computer Conversation with students
The objective of this project is to build embodied conversational agents (also known as ECAs, or virtual humans, or chatbots, or multimodal dialogue systems) that have the ability to engage their users in both social and task talk, where the social talk serves to improve task performance. In order to achieve this objective, we model human-human conversation, and integrate the models into ECAs, and then evaluate their performance. This position is a 3-4 year doctoral contract.
Deadline : 2023-09-30
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Constructing Cyber Security Knowledge Encoding and Reasoning from Heterogeneous Sources with Large Language Models
The Inria Centre at Rennes University is one of Inria’s eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
Deadline : 2023-09-30
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M The role of rapport in human-conversational agent interaction: Modeling conversation to improve task performance in human-agent interaction
The objective of this project is to build embodied conversational agents (also known as ECAs, or virtual humans, or chatbots, or multimodal dialogue systems) that have the ability to engage their users in both social and task talk, where the social talk serves to improve task performance. In order to achieve this objective, we model human-human conversation, and integrate the models into ECAs, and then evaluate their performance. This project is located at the prestigious computer science institute INRIA in downtown Paris. It takes place in the context of the PRAIRIE Institute for Interdisciplinary Research on AI – one of the four 3IA institutes launched by the French government in 2019. For more information see
Deadline : 2023-09-30
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M DOCT2023-STARS Computer Vision / Feature Extraction for Video Understanding using Transformer based Architectures
Inria, the French National Institute for computer science and applied mathematics, promotes “scientific excellence for technology transfer and society”. Graduates from the world’s top universities, Inria’s 2,700 employees rise to the challenges of digital sciences. With its open, agile model, Inria is able to explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of digital transformation. Inria is the source of many innovations that add value and create jobs.
Deadline : 2023-09-30
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Design of flexible and numerically-sound generalised vertical coordinates with vertical ALE (V-ALE) algorithm for operational ocean forecasting
The Nucleus for European Modelling of the Ocean (NEMO) is a geoscientific model, used for a variety of applications covering research on ocean and sea-ice dynamics, operational forecasts (shortrange, seasonal and decadal), re-analyses, climate projections and the preparation of ocean observing systems. The NEMO codebase consists of three main components: the ocean circulation component NEMO-OCE; the sea-ice dynamics and thermodynamics component NEMO-SI3 ; the tracer transport component with an interface for ocean biogeochemistry NEMO-TOP and the biogeochemistry component NEMO-PISCES. NEMO development benefits a broad community of users and interested parties within and beyond the NEMO consortium institutions. The NEMO codebase is used by a community of researchers in universities and academic institutions across the world, and as a component of many systems operated by governmental or international agencies. The proposed work is a contribution to the NEMO development strategy for the period 2023-2027 but its methodological scope goes beyond the specific case of NEMO.
Deadline : 2023-09-15
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Reliable and cost-efficient data placement and repair in P2P storage over immutable data
This PhD thesis will be in the context of a collaboration between HIVE and Myriads and Coast Inria teams. The Ph.D student will be located at Inria Center of the University of Rennes and will be visiting team Coast at Inria Nancy-Grand Est and the Hive offices in Cannes.
Deadline : 2023-09-16
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position F/M Partial differential equation discovery for spatio-temporal simulations in cells
Brain diseases emerge from perturbations of biochemical processes, such as local variations in the concentration of some molecules. Identifying potential treatments (i.e. active drugs) thus requires to understand the sources of dysfunctions at the microscopic scale, which is challenging. First, biological systems are highly complex: they are nonlinear, include unobserved variables, their dynamics occurs on multiple interdependent spatial and temporal scales, and the physical principles that govern their dynamics are partly unknown. Second, data are scarce and some parameter spaces, notably for processes occurring in nanoscopic sub-cellular domains, cannot be reached experimentally. In this context, mechanistic spatially-extended stochastic models are essential to capture the complex chemical interactions that underlie the functioning of brain cells. Notably, they are useful to simulate the mechanism of action of a candidate drug, i.e. its interactions with its cellular targets and its impact on cell dynamics. As this approach is resource and time demanding, it cannot be used to simulate brain dynamics at larger spatio-temporal scales, thus hindering its ability to predict whether the drug of interest is likely to treat the disease. A tool that links such fine-grained microscopic models of drug-cell interactions with brain dynamics at higher temporal and spatial scales is thus of high interest in the search for therapies for brain disorders, yet is currently lacking.
Deadline :2023-09-17
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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