Postdoc: Computational Neurobiology & Single-Cell ML

Harvard University; Cambridge (MA); On-site; USD 68,000 - 80,000; Full time

Job summary

Harvard University’s Arlotta lab invites applications for a Postdoctoral Fellow in Computational Biology. The role centers on computational analysis of multimodal single-cell datasets and collaboration with neuroscience and AI groups to model brain development and neurodevelopmental disorders.

The candidate will work closely with wet-lab scientists, implement advanced analyses (RNA-seq, ATAC-seq, spatial transcriptomics), and contribute to model development using deep learning frameworks.

Qualifications

• Ph.D. degree in a relevant scientific discipline or equivalent experience.
• Experience with statistical analysis of omics data and single-cell methodologies.
• Proficiency in Linux/Unix-based HPC and scripting.

Responsibilities

• Analyze large single-cell datasets to define transcriptomic/epigenetic states in brain models.
• Collaborate with wet-lab biologists to design experiments and validate predictions.
• Develop and maintain computational tools for data analysis and modelling.
• Prepare datasets for modelling and integrative analyses with AI/ML models.
• Present results at team meetings and scientific conferences.

Skills

Statistical analysis

Python/PyTorch

Single-cell data analysis

Linux/Unix HPC

Collaborative teamwork

Education

PhD in Machine Learning, Computer Science, Bioinformatics, Statistics, Biology

Tools

Python

R

SLURM or qsub

Cloud computing (AWS/GCP)

Job description

Harvard University’s Arlotta lab invites applications for a Postdoctoral Fellow in Computational Biology. The role centers on computational analysis of multimodal single-cell datasets and collaboration with neuroscience and AI groups to model brain development and neurodevelopmental disorders.

The candidate will work closely with wet-lab scientists, implement advanced analyses (RNA-seq, ATAC-seq, spatial transcriptomics), and contribute to model development using deep learning frameworks.