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Postdoctoral Fellow, Multimodal Modeling

Biohub· R&D
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Chicago, IL$84.2kpostdoctoralPosted 3mo ago

About the role

Postdoctoral Fellow position at Chan Zuckerberg Biohub Chicago focused on multimodal modeling to uncover spatiotemporal proteoform regulation in inflammation and autoimmunity, integrating confocal imaging, proteomics, phosphoproteomics, and interactomics.

Responsibilities

  • Design and train self-supervised multimodal models that fuse confocal protein imaging, single-cell protein proximity networks, and mass spectrometry-based phosphoproteomics into shared representations, using reconstruction and contrastive objectives (e.g., CLIP)
  • Work with graph-structured proximity-network data, collaborating on graph- and topology-aware modeling approaches
  • Leverage existing high-performing imaging models for feature extraction and inference, adapting them for co-embedding and building new image models where needed
  • Develop cross-modal alignment strategies relating surface organization to signaling and localization, and use the learned representations to model continuous cell-state structure and the features driving state transitions
  • Present findings internally and externally, and co-author publications

Qualifications

  • PhD in machine learning, computational biology, bioengineering, biophysics, or a related field
  • Experience applying deep learning to images, including use of pretrained vision models
  • Demonstrated experience building both supervised and unsupervised models
  • Experience with multimodal modeling or data fusion across heterogeneous data types
  • Experience with graph neural networks or other graph/network representation learning
  • Proficiency in Python and modern deep learning frameworks (e.g., PyTorch, JAX, or TensorFlow)
  • Nice to Have: experience with contrastive or self-supervised learning (e.g., CLIP) for multimodal data
  • Familiarity with topology-aware or higher-order modeling (e.g., simplicial or motif-based methods)
  • Background in proteomics or mass spectrometry data analysis
  • Experience with sequencing-based or single-cell omics data analysis
  • Experience with microscopy or spatial imaging analysis in a biological setting
  • Fluency with immunology or single-cell state modeling
  • Experience building reproducible analysis pipelines and contributing to shared or open-source codebases

Skills

Required:Self-supervised multimodal modelingGraph neural networksImage analysis / pretrained vision modelspython / deep learning frameworksMultimodal data fusion

Benefits

  • Provides a generous employer match on employee 401(k) contributions
  • Paid time off to volunteer at an organization of your choice
  • Funding for select family-forming benefits
  • Relocation support for employees who need assistance moving
Last updated Jul 23, 2026. Interested? Apply on the company site.

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