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Postdoctoral Fellow Machine Learning Jobs in California

Postdoctoral Fellow

Stanford, CA · On-site

$57.60K - $78.10K/yr

Description A Postdoctoral Fellow position is available to analyze alternative splicing machinery and nutrient sensing sponsored by Dr. Shouling Xu at Department of Plant Biology, Carnegie ...

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Postdoctoral Fellow Machine Learning information

What are the key skills and qualifications needed to thrive as a Postdoctoral Fellow in Machine Learning, and why are they important?

To thrive as a Postdoctoral Fellow in Machine Learning, you need a strong background in computer science, mathematics, and statistics, typically supported by a PhD and relevant research experience. Familiarity with programming languages such as Python, machine learning frameworks like TensorFlow or PyTorch, and experience in high-performance computing environments are commonly required. Strong analytical thinking, effective scientific communication, and collaboration skills help you contribute to research teams and disseminate findings. These skills and qualities are crucial for advancing research, developing innovative solutions, and building a successful academic or industry career in machine learning.

What are some common challenges faced by Postdoctoral Fellows in Machine Learning, and how can they be addressed?

Postdoctoral Fellows in Machine Learning often encounter challenges such as balancing independent research with collaborative projects, staying current with rapidly evolving technologies, and securing funding or publishing in top-tier journals. To address these, it's helpful to establish clear communication with mentors and collaborators, set aside dedicated time for reading recent literature, and actively seek feedback on research drafts. Building a professional network through conferences and seminars can also open opportunities for collaboration and career advancement.

What is a Postdoctoral Fellow in Machine Learning?

A Postdoctoral Fellow in Machine Learning is a researcher who has recently completed their PhD and is engaged in advanced research in the field of machine learning. This role typically involves conducting independent or collaborative research, publishing scientific papers, and sometimes mentoring students. Postdoctoral fellows often work at universities, research institutes, or industry labs, focusing on developing new algorithms, improving existing models, or applying machine learning techniques to specific problems. The position is usually temporary, lasting one to three years, and aims to prepare researchers for permanent academic or industry roles.

What is the difference between Postdoctoral Fellow Machine Learning vs Postdoctoral Research Scientist?

AspectPostdoctoral Fellow Machine LearningPostdoctoral Research Scientist
Required credentialsPhD in Computer Science, Data Science, or related fieldPhD in relevant field, often with specialized research experience
Work environmentAcademic labs, universities, research institutionsResearch labs, industry R&D departments, tech companies
Employer and industry usagePrimarily academia, government researchPrimarily industry, corporate research divisions
Common search and comparison intentUnderstanding academic research roles in machine learningExploring industry-focused research career paths

Postdoctoral Fellow Machine Learning roles typically focus on academic research, requiring a PhD and working in universities or research institutions. In contrast, Postdoctoral Research Scientist positions are often industry-based, emphasizing applied research within corporate R&D departments. Both roles involve advanced machine learning expertise but differ mainly in work environment and career trajectory.

What are popular job titles related to Postdoctoral Fellow Machine Learning jobs in California? For Postdoctoral Fellow Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Postdoctoral Fellow Machine Learning jobs in California look for? The top searched job categories for Postdoctoral Fellow Machine Learning jobs in California are:
What cities in California are hiring for Postdoctoral Fellow Machine Learning jobs? Cities in California with the most Postdoctoral Fellow Machine Learning job openings:

Postdoctoral Fellow, Kelley Lab - Gene Regulation Machine Learning

Calico

South San Francisco, CA

$95K/yr

Other

Posted 10 days ago


Job description

Who We Are

Calico (Calico Life Sciences LLC) is an Alphabet-founded research and development company whose mission is to harness advanced technologies and model systems to increase our understanding of the biology that controls human aging. Calico will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Calico's highly innovative technology labs, its commitment to curiosity-driven discovery science and, with academic and industry partners, its vibrant drug-development pipeline, together create an inspiring and exciting place to catalyze and enable medical breakthroughs.

The Calico Postdoctoral Fellowship Program

We are seeking ambitious early career scientists to join us in our mission to increase our understanding of the biology that controls human aging. This is a unique opportunity for individuals to distinguish themselves in aging research, deepen scientific training, and develop research independence.

The Postdoctoral Fellowship is a fixed-term educational assignment (three years with a potential fourth year extension) with rolling start dates. Postdoctoral Fellows are fully integrated into Calico's labs and community.

The program is designed to train individuals to become independent investigators in an academic or industry setting upon successful completion of the assignment. Fellows will receive regular feedback and guidance from their mentor(s) and participate in a formal annual review process to review project progress and individual performance and provide career coaching. 

Responsibilities

  • Lead Independent Research: Focus on their own project(s), which should pursue new research directions with dedicated resources for innovative and potentially high-risk/high-reward research
  • Contribute to Calico's Core Mission: Contribute to furthering an understanding of aging and age-related diseases
  • Drive Scientific Progress: Execute experiments meticulously, analyze data (including computational analysis if applicable) and troubleshoot procedures as needed
  • Publish Work: Generate work that is expected to be submitted for publication in a timely fashion upon completion of the assignment
  • Collaborate and Communicate: Actively collaborate across Calico's diverse teams and scientific community, including presenting progress and results to diverse audiences of computational scientists, lab scientists, and engineers
  • Professional Development: Engage with a community of other Postdoctoral Fellows and senior leadership through professional and career development opportunities, such as the annual Postdoc Summit, research-in-progress presentations, senior leader lunch sessions, and skill-building workshops

Position Requirements

The successful candidate will demonstrate characteristics valued across Calico's programs, including being a self-motivated team player, detail-oriented, extremely organized, and comfortable working on complex problems.

  • Education: Must have recently completed a PhD degree in a relevant scientific or computational field
    • Current PhD student applicants should be within 12 months of completing their PhD
    • Applicants who have already graduated must have completed their PhD within the past 3 years and not have been employed as a postdoc at more than one other lab/company since graduation
  • Experience: Must have made important contributions to science in their area, ideally demonstrated by having had a meaningful first author paper accepted for publication
  • Technical Versatility (General): While specific technical skills are project-dependent, proficiency in core scientific methodologies, analytical skills, and quantitative problem-solving is essential
  • Communication: Strong teamwork and communication skills are required
  • Adaptability: Must be flexible and able to respond quickly to shifting priorities, demonstrating a "can-do" attitude
  • Onsite Availability: Must be willing to work onsite five days per week

Application Requirements

The Postdoctoral Fellowship has four application components:

  • Research proposal (a 1-2 page creative and original research proposal on what you'd like to investigate in the Kelley Lab during your fellowship - note that project goals may evolve during the training period)
  • Updated CV with a full list of publications
  • Cover letter
  • List of 2-3 references that we may contact, including your PhD thesis advisor and any postdoctoral advisors (if applicable)

The estimated base salary for this role is $95,000. More information on our postdoctoral fellowship program can be found here.

About the Kelley Lab

Our group develops deep learning methods for regulatory genomics. The goal is a comprehensive model of the human cis-regulatory code that predicts how every nucleotide in the genome affects cell-type-specific gene regulation. We apply these models to predict genetic variant effects, amplifying insights from human genetics by boosting statistical power across the allele frequency spectrum, pinpointing causal variants, and generating mechanistic hypotheses.

Calico's structure means close collaboration with experimental groups and access to rich longitudinal phenotyping across large human cohorts, which lets us connect sequence-level predictions to outcomes that matter for aging. To fully leverage this, we seek highly quantitative, deeply curious, and intellectually courageous collaborative team players who thrive in a multidisciplinary environment. We welcome proposals that address core questions in the application of machine learning to derive insights from genomics, connecting sequence-level predictions to outcomes that matter for aging.

Publications from the group include:

  • Ziga Avsec et al., Effective gene expression prediction from sequence by integrating long-range interactions (2021) Nature Methods
  • Han Yuan & David R. Kelley, scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks (2022) Nature Methods
  • Johannes Linder, Divyanshi Srivastava, Han Yuan, Vikram Agarwal & David R. Kelley, Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) Nature Genetics