Postdoctoral Researcher F/H in Connectivity Model Generation
il y a 3 semaines
The Inria Saclay research center was established in 2008 and plays a vital role 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 key partners to foster an ambitious site policy.
The center comprises 40 project teams, with 32 teams working in partnership with the University of Paris-Saclay or the Institut Polytechnique de Paris. It engages over 600 individuals, including researchers and support staff from 54 different nationalities.
Inria Saclay - Île-de-France is a key player in digital science research on the Saclay plateau, embodying the values and projects that distinguish Inria in the research landscape: scientific excellence, technological transfer, and multidisciplinary partnerships to maximize 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. The project will involve generating samples from large 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 GAN performance.
Travel Requirements: No regular travel is expected for this position.
Assigned Responsibilities
The selected candidate will undertake technical developments, implementing various generative models for covariance matrices utilized in functional connectivity analysis.
• The R-CNN network integrated into score-based generative modeling can learn real images at the pixel level, yielding highly realistic covariance matrices.
• Graph neural networks may provide effective representations of node relationships, although 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 detailed exploration, potentially in conjunction with Riemannian score-based generative modeling.
The generative model must be conditional, tailored to input information such as age, sex, and characteristics of the target population, including disease status. Subsequently, the candidate will systematically assess the quality of the generated connectivity models through qualitative evaluations and metrics to determine coverage, recall, and fidelity.
Validation will be conducted on brain imaging datasets, training the generative model on the extensive UKBiobank time series. The validation process will include assessing whether the effects of covariates or treatments can be captured and reproduced by the generated data, particularly in creating explicit counterfactuals and evaluating the utility of data generation in downstream prediction tasks.
Once quality standards are met, the model will be applied to augment datasets from other cohorts with similar population profiles. The adaptation of generators to new cohort settings will be studied, addressing potential covariate shifts through Riemannian approaches and optimal transport techniques.
Main Activities
The project will produce experimental code in Python, made available as an open-source initiative to ensure scientific reproducibility. Compatibility with existing frameworks, such as Nilearn, will be prioritized. The most effective components of the code may be integrated into Nilearn or Geomstats. Experiments will utilize data stored on protected MIND servers.
Required Skills
Technical skills and expertise required: 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 RTT days (full-time basis) + possibility of exceptional leave (e.g., for sick children, relocation)
- Remote work options and flexible working hours
- Professional equipment available (videoconferencing, computer equipment loans, etc.)
- Social, cultural, and sports benefits (Inria social works management association)
- Access to professional training
- Social security
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