Postdoctoral Researcher
il y a 1 semaine
Research Context
The successful candidate will be part of a research team at INRIA, working on the development of mathematical frameworks and practical setups for applying Topological Data Analysis (TDA) methods, descriptors, and features in the context of deep generative models. The goal is to design and implement novel generative models that incorporate topological information, enabling the fast and accurate generation of galaxy maps from cosmological data.
Key Responsibilities
- Develop and implement TDA-based generative models, focusing on multimodal variational autoencoders with topology-adapted reparameterization tricks.
- Study the theoretical differentiability properties of TDA descriptors based on multi-parameter persistence and its implementation in generative models.
- Apply the developed models to the fast generation of approximate yet accurate galaxy maps and constrain physical parameters corresponding to real observations.
Requirements
- PhD degree in applied mathematics and/or computer science.
- Excellent programming skills with extensive knowledge of data science libraries for machine learning and optimization (e.g., Scikit-Learn, TensorFlow, PyTorch).
- Research experience in deep generative models, machine learning, dimensionality reduction, and non-convex optimization.
- Experience in geometric and topological methods in machine learning and approximate cosmological simulations is a plus.
Working Conditions
- Located at INRIA, with regular meetings organized with collaborators in Area Science Park, Trieste, Italy.
- Subsidized meals, partial reimbursement of public transport costs, and flexible organization of working hours.
- Professional equipment available, including videoconferencing and loan of computer equipment.
Language
English (fluent), French (optional)
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