About the position
About the research centre or Inria department The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’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 regional 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. Context This position is in the context of our ongoing ERC Advanced Grant NERPHYS (for more information see https://www.inria.fr/en/erc-grants-george-drettakis-ai-physics-3d and https://project.inria.fr/nerphys/ ) The are seeking to hire a highly motivated candidate who will be part of this exciting project, involving software engineering work on novel research projects, while working alongside the GRAPHDECO group that is continuing the advancement of new research ideas in this project. This position is a great opportunity to be part of a world-class team of researchers working on exciting and timely projects. The successful candidate will acquire top-notch first-hand knowledge and experience in radiance field rendering which is in extremely high demand today, providing excellent skills for career enhancement. The engineer will work on projects of the Ph.D. students and postdocs of the group. Context Generative models have proven highly effective at providing powerful structural and physical priors for complex computer graphics and vision tasks. Recent breakthroughs leverage these generative priors to tackle inverse problems, such as intrinsic image decomposition [1], radiance field relighting [2], and physical dynamic reconstruction. Most state-of-the-art approaches build on large video or multi-view diffusion models pre-trained on massive datasets. While fine-tuning these models preserves generalization while adapting them to domain-specific physics and rendering constraints, doing so reliably requires robust data pipelines, dynamic simulation grounding, and large-scale data collection strategies. Within the ERC Advanced Grant NERPHYS (NEural Representations for PHYSical simulation) , the engineer will help build solutions that aim to bridge neural representations (e.g., 3D Gaussian Splatting, NeRFs) with physics-based simulation and generative priors to achieve interactive, physically plausible 3D scene editing and simulation. Assignment Approach & Engineering Scope As a Research Engineer on the NERPHYS project, the work will focus on designing, implementing, and scaling the core computational and data infrastructure: Data Strategy & Infrastructure: Define and deploy automated data collection, synthetic rendering, and validation pipelines tailored for fine-tuning dynamic generative and physical models. Model Fine-Tuning & Integration: Implement GPU-accelerated training workflows based on physically-based rendering to fine-tune diffusion models and integrate them with implicit/explicit neural scene representations (3DGS/NeRFs) and physical solvers. Systems & Tooling: Build robust, reusable software abstractions and benchmarking frameworks to support the research team in running reproducible dynamic simulation experiments. Key References [1] Liang, Ruofan, et al. "Diffusionrenderer: Neural inverse and forward rendering with video diffusion models." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2025. [2] Poirier‐Ginter, Yohan, et al. "A Diffusion Approach to Radiance Field Relighting using Multi‐Illumination Synthesis." Computer Graphics Forum , Vol. 43, No. 4, 2024. [3] ERC Grant NERPHYS: NEural Representations for PHYSical simulation (Inria GraphDeco). Skills We are searching for recent graduates, but also for candidates with a few years experience. Requirements Education: Master’s degree in Computer Science, specializing in Computer Graphics, and Machine Learning for visual computing. Computer Vision education is a plus. Core Skills: Proficiency in Python, PyTorch, C++, and CUDA for high-performance deep learning and graphics workflows. Graphics & Physics Expertise: Background in computer graphics (rendering, rasterization, ray tracing, path tracing, mitsuba3). Advanced Machine Learning skills: Experience with training/fine tuning large models, in particular diffusion models and transformers, automated data generation pipelines, multi-GPU scaling Experience with use of AI agentic coding frameworks for task automation.
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