Stage: AI-powered multimodal integration of spatial omics data

• State-of-the-art Benchmarking: Evaluate existing multi-modal integration frameworks (e.g., SpatialGlue, GLUE, or Seurat v4) on public and in-house paired ST+MSI datasets to identify specific failure points in metabolic signal alignment.
• Methodological Development: Design and implement a novel Graph Neural Network (GNN) architecture to bridge the identified gaps.
• Implementation of Joint Embeddings: Develop an automatic supervised graph-learning workflow to map both modalities into a shared latent space, ensuring robustness against differences in spatial resolution and technical noise.
• Validation: Apply the developed model to in-house PDX glioblastoma datasets to characterize ROIs and validate the biological relevance of the integrated signal in collaboration with our cancer biology partners.
• Software Documentation: Deliver a clean, well-documented Python codebase (compatible with the lab's existing DIMet/SpacePath ecosystem)., * Galvis J, Guyon J, Dartigues B, Hecht H, Grüning B, Specque F, Soueidan H, Karkar S, Daubon T, Nikolski M. DIMet: An Open-Source Tool for Differential Analysis of Targeted Isotope-Labeled Metabolomics Data. Bioinformatics 2024 40 (5) btae282. https://doi.org/10.1093/bioinformatics/btae282.
• Galvis J, Guyon J, Daubon T, Nikolski M. Using DIMet for Differential Analysis of Labeled Metabolomics Data: A Step-by-Step Guide Showcasing the Glioblastoma Metabolism. Bio-Protoc. 2025 15 (2) e5168. https://doi.org/10.21769/BioProtoc.5168.
• Ravi VM, Will P, Kueckelhaus J,… & Heiland DH Spatially Resolved Multi-Omics Deciphers Bidirectional Tumor-Host Interdependence in Glioblastoma. Cancer Cell 2022 40 (6) 639-655.e13. https://doi.org/10.1016/j.ccell.2022.05.009.
• Wheeler K, Gosmanov C, Jimenez Sandoval M, Yang Z, McCall LI. Frontiers in Mass Spectrometry-Based Spatial Metabolomics: Current Applications and Challenges in the Context of Biomedical Research. TrAC Trends Anal. Chem. 2024 175 117713. https://doi.org/10.1016/j.trac.2024.117713.
• Liu T, Fang Z.-Y, Zhang Z, Yu Y, Li M, Yin M.-Z. A Comprehensive Overview of Graph Neural Network-Based Approaches to Clustering for Spatial Transcriptomics. Comput. Struct. Biotechnol. J. 2024 23 106-128. https://doi.org/10.1016/j.csbj.2023.11.055.

• M2 / engineering school student in Computer Science, Applied Mathematics, AI, bioinformatics.
• Solid Python skills, including familiarity with deep learning frameworks (PyTorch, TensorFlow) and/or LLM APIs (Huggingface, OpenAI).
• Knowledge of machine learning.
• Prior exposure to omics data is a plus but not necessary.
• Background in molecular/cellular biology or metabolism is a plus; motivation to work in an interdisciplinary setting is essential.

The internship will provide hands-on experience at the intersection of AI and computational biology.

Despite recent advances in multi-modal spatial omics integration, metabolic semantics are still poorly used by existing frameworks: the key molecular modality (MSI-based spatial metabolomics/lipidomics) differs from ST in resolution, quantitativeness and section-to-section registration [4]. Most ST-MSI integration across adjacent sections relies on geometric registration (histology-derived anchors), however, invasive cells are sparse and white-matter anatomy is anisotropic, which can lead to biological signal misalignment. On the other hand, graph-based fusion methods (e.g., SpatialGlue, SpatialMET, COSMOS) improve integration in more homogeneous settings.