Postdoctoral Researcher, Konermann & Goodarzi Labs

Arc Institute • Palo Alto, CA

11h • $80,000

About The Position

We are seeking an exceptional computational postdoctoral fellow to join the Konermann and Goodarzi laboratories at the Arc Institute. This is a unique opportunity to work at the intersection of AI/ML, functional genomics, precision medicine, and single cell multiomics to develop and apply cutting-edge machine learning tools for understanding causal drivers of complex biological systems.
In this joint position, you will contribute to developing broadly applicable computational frameworks for integrating multi-omics data, building predictive models of cellular behavior, and scaling phenotypic discovery in disease-relevant contexts, such as Alzheimer’s disease. Your work will leverage Arc’s state-of-the-art experimental platforms and collaborative environment.

Requirements

• PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, Statistics, Bioengineering, or a related quantitative field.
• Strong publication record demonstrating expertise in computational analysis of genomics data, particularly single-cell technologies.
• Extensive experience with Python, machine learning frameworks, and distributed training, including active and well-maintained projects on public repositories.
• Experience with machine learning and statistical modeling, particularly applied to biological data.
• Strong understanding of molecular biology, with ability to design analyses that directly address biological questions.
• Proven ability to work independently and collaboratively in interdisciplinary teams.
• Excellent written and oral communication skills, with demonstrated ability to present complex computational work to diverse audiences.
• Track record of completing projects and publishing results in peer-reviewed journals.

Nice To Haves

• Experience with pooled screening technologies (CRISPR screens, perturbation screens, drug screens) and associated computational modeling.
• Experience with multi-omics data integration and analysis.
• Experience with cloud computing and scalable data analysis workflows.
• Contributions to open-source software or publicly available computational tools.
• Experience with spatial transcriptomics or other emerging single-cell technologies.

Responsibilities

• Develop and apply AI/ML frameworks for modeling multi-omics datasets, with emphasis on scalable approaches for phenotypic screening and disease modeling.
• Design and implement causal machine learning methods for predicting cellular states, drug responses, and disease phenotypes from genomic data.
• Collaborate closely with experimental teams to design studies, optimize protocols, and integrate computational and experimental workflows.
• Present research findings at internal meetings, seminars, and external conferences.
• Mentor junior researchers, graduate students, and contribute to Arc’s collaborative scientific culture.
• Publish high-impact research in leading scientific journals.

What This Job Offers

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