博士研究生岗位（男女均可，F/M）：用于预测个体认知的多模态神经影像预训练模型

专注于开发神经影像数据机器学习模型的计算神经科学博士研究生岗位。要求具备 Python 及深度学习框架的使用能力；有神经影像相关经验者优先。

Assignment: With the help of D. ［会员专属信息］ the recruited person will conduct a PhD thesis on the following research: After fifty years of neuroimaging, the relationship between brain organisation and primary sensorimotor function is well characterised: replicable, lesion-validated and spatially precise. Higher-order cognition is a different matter. Working memory, executive control, spatial attention and language recruit the same distributed regions across entirely different paradigms, producing overlapping activation maps that resist segregation. Large-scale databases such as the Human Connectome Project (n ≈ 1,200) and the UK Biobank (n > 40,000) show that multimodal neuroimaging phenotypes predict composite cognitive scores with correlations approaching r = 0.5. However, the experiments with the highest cognitive specificity rarely exceed 50–200 participants, a regime in which deep learning models cannot be trained from scratch. Cognitive neuroscience needs pretrained neuroimaging models that transfer to any dataset, however small. The MIND team and the GIN Bordeaux team are building such models within an ANR-funded collaboration. The recruited PhD student will develop the machine learning core of this effort: pretrained models of multimodal neuroimaging (functional MRI, diffusion MRI and structural MRI) that predict individual cognitive phenotypes and quantify the uncertainty of their predictions. The thesis has three objectives. First, the student will build the data infrastructure on which the models are trained: open preprocessing and harmonisation pipelines for large multimodal databases such as the Human Connectome Project and the UK Biobank, covering functional, diffusion and structural MRI together with their cognitive batteries. Second, the student will design and benchmark deep generative and self-supervised models that learn representations of the multimodal brain phenotype from these databases and predict cognitive outcomes from them, with principled uncertainty quantification. Third, the student will study how these representations transfer to the small and medium-sized datasets that make up most of cognitive neuroscience, comparing transfer learning and domain adaptation strategies under rigorous cross-validation, and release the resulting benchmarks openly. The central methodological challenge of the thesis is to learn representations that are both faithful to the neuroimaging signal and relevant to cognition, at the scale of tens of thousands of subjects, while keeping the uncertainty of the resulting predictions calibrated. Reaching this goal draws on the MIND team's experience in amortised variational inference (PAVI), likelihood-free inference for brain microstructure, and transfer learning of individualised functional parcellations, and on the joint work of the two teams on the geometry of brain–cognition organisation (Pacella et al., 2024). At the end of the thesis, the student will have produced openly released pretrained multimodal models with uncertainty quantification, the open preprocessing and training code that reproduces them, and an open transfer learning benchmark with practical recommendations for deploying the models on small cognitive neuroscience datasets. A short video presenting the overall project is available at ［会员资料链接］ . For a better knowledge of the proposed research subject we recommend the following literature: Pacella, V.; Thiebaut de Schotten, M.; ［会员专属信息］, D. et al. The morphospace of the brain–cognition organisation. Nature Communications 2024, 15, 8452, DOI: 10.1038/s41467-024-52186-9. Rouillard, L.; Moreau, T.; ［会员专属信息］, D. PAVI: Plate-Amortised Variational Inference. Transactions on Machine Learning Research 2022, DOI: 10.48550/arXiv.2206.05111. Le Bris, A. et al. Improving Individual-Specific Functional Parcellation Through Transfer Learning. Preprint 2024. Jallais, M.; Rodrigues, P. L. C.; Gramfort, A.; ［会员专属信息］, D. Cytoarchitecture Measurements in Brain Gray Matter Using Likelihood-Free Inference. In Information Processing in Medical Imaging (IPMI), Springer, 2021, pp 191–202, DOI: 10.1007/978-3-030-78191-0_15. Abdallah, M.; Zanitti, G. E.; Iovene, V.; ［会员专属信息］, D. Functional Gradients in the Human Lateral Prefrontal Cortex Revealed by a Comprehensive Coordinate-Based Meta-Analysis. eLife 2022, 11, e76926, DOI: 10.7554/eLife.76926. Menon, V.; Gallardo, G.; Pinsk, M. A.; Nguyen, V.-D.; Li, J.-R.; Cai, W.; ［会员专属信息］, D. Microstructural Organization of Human Insula Is Linked to Its Macrofunctional Circuitry and Predicts Cognitive Control. eLife 2020, 9, e53470, DOI: 10.7554/eLife.53470. Ooi, L. Q. R. et al. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. NeuroImage 2022, 263, 119636, DOI: 10.1016/j.neuroimage.2022.119636. Ooi, L. Q. R. et al. Longer scans boost prediction and cut costs in brain-wide association studies. Nature 2025. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 2016, 536, 171–178, DOI: 10.1038/nature18933. Tavor, I. et al. Task-free MRI predicts individual differences in brain activity during task performance. Science 2016, 352, 216–220, DOI: 10.1126/science.aad8127. Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 2013, 80, 62–79, DOI: 10.1016/j.neuroimage.2013.05.041. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience 2016, 19, 1523–1536, DOI: 10.1038/nn.4393. Poldrack, R. A. Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences 2006, 10, 59–63, DOI: 10.1016/j.tics.2005.12.004. Main activities: Main activities (5 maximum) : Build and release open preprocessing and harmonisation pipelines for large multimodal neuroimaging databases (fMRI, diffusion MRI, structural MRI and cognitive batteries) Design, train and benchmark deep generative and self-supervised models of the multimodal brain phenotype that predict cognitive outcomes with calibrated uncertainty Evaluate transfer learning and domain adaptation strategies on small and medium-sized cognitive neuroscience datasets and release an open benchmark suite Validate the advances and write scientific literature on them, targeting NeurIPS, ICML, MICCAI, OHBM and journals such as Nature Methods or NeuroImage Release models and code openly and interact with the GIN Bordeaux partner team

技能要求： 所需的技术技能与水平： 对 Python 编程及至少一种深度学习框架（PyTorch 或 JAX）有扎实的掌握能力 具备数学形式化方法以及机器学习和人工智能的规范背景：概率论、统计学、变分推断与表示学习 具备神经影像数据（功能性磁共振成像、扩散磁共振成像）及其分析工具（如 nilearn、fMRIPrep、MRtrix、DIPY）的相关经验尤为理想 具备大规模计算相关经验（SLURM 集群、多 GPU 训练）尤为理想 语言： 期望申请者能够熟练使用英语进行交流。无需掌握法语。 成功的关键： 您应提供所寻求合作者“总体轮廓”，明确阐述您认为必要且充分的条件，并说明可能的组合方式。 对人工智能与机器学习以及神经科学均充满热情 熟悉 PyTorch 等工具及人工智能的数学基础。 对团队合作的偏好 本部分有助于完成更为正式的技能清单，并使其“简化”：（减少）相关内容。

福利包： 补贴餐食 部分公共交通费用报销 休假安排：7周年度假 + 因RTT（法定工作时间减少）额外获得的10天假期 + 可能的特殊假期（如照顾生病的子女、搬家等） 远程工作及灵活安排工作时间的可能性 提供专业设备（视频会议、计算机设备借用等） 社会、文化和体育活动及项目 获取职业培训机会 社会保障覆盖范围 薪酬： 每月总收入为2300欧元 申请该职位

申请说明： 国防安全：本岗位可能位于受限区域（ZRR），此类区域依据保护国家科学技术潜力（PPST）的第2011-1425号法令界定。根据2012年7月3日关于PPST的法令，在主管部门作出批准决定后，由单位负责人授予区域准入许可。对于位于ZRR内的岗位，若主管部门作出不予批准的决定，将取消聘用。 招聘政策：作为多样性政策的一部分，法国国家信息与自动化研究所（Inria）的所有岗位均向残疾人士开放。