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Postdoctoral Researcher F/H in Connectivity Model Generation
Il y a 2 mois
The Inria Saclay research center was established in 2008 and is a key player in the development of the Saclay plateau, closely collaborating with the University of Paris-Saclay and the Institut Polytechnique de Paris. In 2021, strategic agreements were signed with these two prominent partners to foster an ambitious site policy.
The center hosts 40 project teams, with 32 being joint teams with the University of Paris-Saclay or the Institut Polytechnique de Paris. It brings together over 600 individuals, including researchers and support staff from 54 different nationalities.
The Inria Saclay - Île-de-France center is a vital contributor to research in digital sciences on the Saclay plateau, embodying the values and projects that define Inria's uniqueness in the research landscape: scientific excellence, technological transfer, and multidisciplinary partnerships with institutions that complement our expertise to maximize Inria's scientific, economic, and societal impact.
Context and Advantages of the Position
Within the framework of a partnership for the PEPR Brein Health Trajectories project.
The primary objective of this initiative is to develop a generative methodology for covariance models that aligns with the Riemannian framework for connectivity modeling in fMRI. This involves generating samples from extensive populations and fine-tuning models for smaller groups. The quality of the generated samples will be evaluated using various metrics, including authenticity, coverage, recall, and assessments related to GAN training and testing. Ultimately, the utility of this generative approach will be examined in relation to longitudinal and cross-sectional connectivity-based diagnostic challenges.
Regular travel required for this position? No
Assigned Mission
The candidate will focus on technical advancements by implementing various generative models for covariance matrices utilized in functional connectivity analysis.
• The R-CNN network integrated within score-based generative modeling can learn real images at the pixel level, producing highly realistic covariance matrices.
• Graph neural networks are capable of generating effective representations of node relationships, though the realism of the generated covariance matrices remains an open question.
• A well-established method known as stable diffusion maps data to latent space through an encoder, followed by the application of a diffusion model. Specific neuroimaging tasks will require further exploration of numerous details. This can be combined with Riemannian score-based generative modeling for additional experimentation.
The generative model must be conditional, tuned to specific input information such as age, sex, and characteristics of the target population, including disease status.
In the subsequent phase, the candidate will systematically assess the quality of the generated connectivity models through qualitative evaluations and metrics to determine coverage, recall, and fidelity.
Validation on brain imaging datasets will involve training the generative model on the extensive UK Biobank time series. The validation process will include evaluating whether the effects of certain covariates or treatments can be captured and reproduced by the generated data, particularly in creating explicit counterfactuals, as well as assessing the utility of data generation in downstream prediction tasks.
Once the quality meets expectations, the approach will be applied to augment datasets from other cohorts with similar population profiles. The adaptation of generators to the specific conditions of new cohorts will be a focus, addressing potential covariate shifts through Riemannian approaches and optimal transport techniques.
Main Activities
The project will produce experimental code in Python, which will be made available as an open-source initiative to ensure scientific reproducibility. Emphasis will be placed on compatibility with existing frameworks such as Nilearn. Notable components of the code may be integrated into Nilearn or Geomstats. Experiments will be conducted using data secured on MIND servers.
Required Skills
Technical expertise required includes proficiency in Python, deep learning, and brain imaging.
Languages: English
Interpersonal skills: Team collaboration
Additional appreciated skills: -
Benefits
- Subsidized meals
- Partially reimbursed public transport
- Leave: 7 weeks of annual leave + 10 days of RTT (full-time basis) + possibility of exceptional leave (e.g., for sick children, relocation)
- Remote work options and flexible working hours
- Professional equipment available (videoconferencing, loan of IT materials, etc.)
- Social, cultural, and sports benefits (Inria's social works management association)
- Access to professional training
- Social security
Based on profile
