Postdoctoral Researcher, Zhou Lab

Posted 16 Days Ago
Be an Early Applicant
Palo Alto, CA, USA
In-Office
80K-80K Annually
Expert/Leader
Artificial Intelligence • Healthtech • Machine Learning • Biotech
The Role
The Postdoctoral Researcher at Zhou Lab will develop machine learning models for biological data, specifically in genomics and gene regulation, collaborate with experimental biologists, and lead research projects for significant scientific discoveries.
Summary Generated by Built In

About Arc Institute

Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.

Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.

Our two Institute Initiatives reflect this model in action:

  • Virtual Cell Initiative: Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.
  • Alzheimer's Disease Initiative: Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.

More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.

About the position

The Zhou Lab is looking for motivated, hard-working and curious applicants. Our expertise is in single-cell epigenomic modeling. We use high-throughput single-cell multiomic technologies and build computational models to study spatiotemporal dynamics of gene regulation.

The successful candidate will play a crucial role in advancing the state-of-the-art in generative AI applied to biology, including DNA sequence, gene regulation, and perturbation modeling. You will focus on developing ML models for biological data, leveraging frontier approaches such as novel ML architectures, interpretability techniques, and more. You will also apply your models for important computational biology applications in genome mining, molecular technology development, and invention of new therapeutic approaches.

This role is a unique opportunity to advance state-of-the-art machine learning in genomics and cell biology, contribute to high-impact scientific discoveries, and help define how computational approaches shape our understanding and engineering of biology. You will work in a highly collaborative team with expert experimental biologists to realize the full impact of your work, and also have the opportunity to contribute to Institute-wide machine learning efforts such as Arc’s Virtual Cell Initiative.

Post-docs will be encouraged to lead independent projects resulting in high impact publications, present at conferences and prepare for long-term careers in academia or industry.

About you

  • You are passionate about developing machine learning models with real-world applications and scientific impact.
  • You have a strong understanding of modern deep learning, computational biology, and genetics.
  • You are known for your ability to analyze/visualize complex datasets, build high-quality ML tools, draw meaningful conclusions, and work effectively in a multidisciplinary team. 
  • You are eager to learn and adapt new techniques.
  • You are intellectually independent and are able to design new research directions and projects with input from your PI.
  • You are excited about collaborating with a multidisciplinary team of experimental biologists and machine learning researchers at Arc.
  • You are familiar with recently reported projects in the fields of AI for genomics and biological design, including DNA sequence to function models, genome language models, and single-cell foundation models.

In this position you will

  • Train and evaluate state-of-the-art machine learning models for genomics 
  • Design, perform, and analyze experiments for model applications
  • Keep appropriate experimental records and documentation
  • Analyze results with the Principal Investigator
  • Collaborate with postdocs, students, and employees on model development and data analyses
  • May mentor/train research associates, technicians, and students. 
  • Publish, present, and represent the Zhou lab in journals and conferences.
  • Present at lab meetings, and participate in Arc-wide activities (seminars, science socials, symposiums, etc).

Requirements

  • Doctorate (MD, PhD) in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Genetics/Genomics. 
  • Excellent written and verbal communication skills.
  • Demonstrated ability to work in a fast-paced environment and be both an independent thinker and a highly collaborative team player.

How to Apply

Please submit:

  1. CV
  2. Brief cover letter describing your research interests, what kinds of projects you’re excited to lead, and why the Zhou Lab is a fit. Please be sure to include contact information for 3+ references.

The minimum base salary for this position is $80,000. Base salary for this role is determined by how many months of relevant postdoctoral experience a successful candidate has. Base salary for this role is not negotiable.

Skills Required

  • Doctorate in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Genetics/Genomics
  • Excellent written and verbal communication skills
  • Demonstrated ability to work in a fast-paced environment and be an independent thinker and collaborative team player
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The Company
250 Employees
Year Founded: 2021

What We Do

Arc Institute is a nonprofit research organization focused on curiosity-driven biomedical science and technology, aiming to accelerate scientific progress, understand the root causes of complex diseases, and narrow the gap between discoveries and patient impact.

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