Postdoctoral Fellow, Multimodal Modeling

Biohub • Chicago, IL

5d • Onsite

About The Position

The Chan Zuckerberg Biohub Chicago is seeking outstanding early-career scientists to join and participate in the launch of the Proteoform Spatial Biology Group by continuing their training as a Postdoctoral Fellow in Multimodal Modeling. The Proteoform Spatial Biology Group aims to uncover the spatiotemporal regulation of proteins and their unique molecular forms, proteoforms, in inflammation and autoimmunity. This role involves designing and training self-supervised multimodal models that fuse diverse biological data types, including confocal microscopy images, single-cell protein proximity networks, and mass spectrometry-based phosphoproteomics. The goal is to develop shared representations that can model continuous cell-state structure and the features driving state transitions, ultimately contributing to a deeper understanding of inflammation and autoimmunity.

Requirements

• 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 Haves

• 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.

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 objectives such as reconstruction and contrastive alignment (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.

Benefits

• 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.
• Discretionary annual performance bonus program.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree