Young Investigator, Open Language Models for Biology

Your Next Challenge

Develop a multimodal language model that combines single-cell gene-expression data with text, using an open model such as OLMo. Reproduce and evaluate relevant approaches, including scGPT, Geneformer, CellWhisperer, and C2S-Scale. Adapt and improve these approaches for brain and neurodegeneration data. Design controlled experiments to test whether language grounding improves performance over cell-only and text-free baselines. Develop representations at the brain-region and donor levels that incorporate pathology and disease progression. Evaluate models on held-out donors using measures such as cell-type and marker recovery, agreement with known patterns of regional vulnerability, and expert review of plain-language answers. Work with Allen Institute scientists to interpret results and identify predictions suitable for experimental follow-up. Release data, code, and model weights, and prepare the results for publication.

What You’ll Need

• A completed PhD, or a PhD expected within one year, in machine learning, computational biology, computational neuroscience, physics, or a related quantitative field.
• Hands-on experience training or fine-tuning transformer-based language models.
• Strong Python and PyTorch skills.
• Experience writing reliable, reproducible research code.
• The ability to carry out independent research on open-ended problems.
• The ability to communicate clearly with both machine-learning researchers and biological scientists.

We also look favorably upon experience in one or more of the following areas:

• Single-cell genomics, including scRNA-seq or snRNA-seq.
• Tools such as scanpy, AnnData, or the scVI ecosystem.
• Multimodal models, instruction tuning, or language-model post-training.
• Neurodegeneration, brain atlases, or spatial transcriptomics.
• Public releases of research code, datasets, or model weights.