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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 6 days ago
applied mathematics, control, or related fields, with knowledge in several of the following areas: Graph theory and network modeling Dynamical systems and physical modeling (ODE/PDE, multi-agent systems
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). The successful candidate will contribute to the development, optimization, and application of PET-CT imaging protocols for small animal models included in quantitative multi-imaging modalities. Responsibilities
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in high performance scientific computing, multi-linear algebra and tensor contractions for heterogeneous exascale architectures. The successful candidate will join the NumPEx PEPR to reinforce
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the graph structure into account. Other data fusion or anomaly detection methods, such as multi-modal autoencoders, may be considered depending on the candidate's skills. [1] Barkat Ishak and Lin-Kwong-Chon
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platform. The areas of application cover major economic sectors: the semiconductor industry, information technologies, life and health technologies, energy and the environment. The laboratory is multi-site
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of axion production in dense media, with a particular emphasis on gamma-ray signatures, gravitational waves, and multi-messenger observables. Qualifications: Applicants should have: a PhD in physics or
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16 Apr 2026 Job Information Organisation/Company CNRS Department Centre international de rencontres mathématiques Research Field Anthropology Anthropology » Ethnology Researcher Profile First Stage
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occurs via small-scale extreme events that propagate to large scale. A detailed check of this hypothesis thus requires multi scale tools and dedicated experimental setups as well as the development
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group (depending on the profile, materials under pressure, MOF coordination polymer design, multi-objective optimization Tc/HEDM/reactivity, etc.). - Calculating the electronic structures
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whole-genome and exome sequencing. The group also integrates multi-omics data through nonlinear data fusion approaches and leverages pre-trained DNA language models as unsupervised feature extractors