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Plant Genomics Postdoc Jobs (NOW HIRING)

University of California

Postdoctoral Scholar in AI and Foundation Models for Plant Genomics The UC Davis Genome Center is recruiting a Postdoctoral Scholar to work on a collaboration between the laboratories of Richard ...

The Pennsylvania State University

POSITION SPECIFICS The Pennsylvania State University Department of Plant Pathology and ... The successful candidate will develop and apply molecular, genomic, and computational approaches to ...

Zymtronix Catalytic Systems Inc

NY · On-site

$62K - $88K/yr

Postdoctoral Associate - Plant Pathology & Plant Microbe Biology Position Function This position ... The individual is required to use tools in chemical ecology, genomics, molecular biology, and AI ...

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How much do plant genomics postdoc jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for plant genomics postdoc in the United States is $18.24, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $18.27 per hour, depending on experience, location, and employer.

What is a plant genomics postdoc?

A Plant Genomics Postdoc is a researcher who has recently earned a Ph.D. and is engaged in advanced studies focusing on the genetics and genomics of plants. They typically work in academic, government, or industry research laboratories, applying techniques like DNA sequencing, gene editing, and bioinformatics to understand plant biology. Their work often includes designing and conducting experiments, analyzing data, and publishing scientific papers. The goal is to contribute to advancements in crop improvement, plant breeding, and fundamental plant science.

What are the key skills and qualifications needed to thrive as a plant genomics postdoc?

To thrive as a Plant Genomics Postdoc, you need a Ph.D. in plant biology, genetics, or a related field, with strong expertise in molecular biology, genomics, and data analysis. Proficiency in bioinformatics software, next-generation sequencing platforms, and programming languages like R or Python is typically required. Critical thinking, problem-solving, and effective scientific communication are standout soft skills in this role. These abilities are essential for advancing research, publishing findings, and collaborating successfully in multidisciplinary environments.

What are typical collaborative opportunities for a plant genomics postdoc within a research institution?

As a Plant Genomics Postdoc, you’ll often collaborate closely with interdisciplinary teams, including molecular biologists, bioinformaticians, and plant breeders. These collaborations usually involve joint research projects, data analysis, and sharing resources such as sequencing platforms or field sites. You may also participate in lab meetings, present findings to other research groups, and contribute to multi-author publications. Such teamwork not only enhances the quality of research but also helps you build a strong professional network, which is valuable for career advancement.

What are popular job titles related to Plant Genomics Postdoc jobs?

For Plant Genomics Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Plant Genomics Postdoc job openings in the United States as of September 2026, with employment types broken down into 90% Full Time, 6% Part Time, 1% Contract, and 3% Nights. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $37,934 per year, or $18.2 per hour.

Post Doctoral Scholar - Michelmore Lab

Davis, CA • On-site

University of California
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Posted 10 days ago

University Of California rating

8.7

Company rating: 8.7 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

Job description

Postdoctoral Scholar in AI and Foundation Models for Plant Genomics


The UC Davis Genome Center is recruiting a Postdoctoral Scholar to work on a collaboration between the laboratories of Richard Michelmore (Genome Center), Xin Liu (Computer Science), and Christine Diepenbrock (Plant Sciences). This project will develop one of the first crop-specific multimodal foundation models integrating more than 100 telomere-to-telomere lettuce genomes, population-scale genomic variation, transcriptomics, and extensive phenotypic datasets to predict the consequences of allelic variation, genome editing, and genotype-by-environment interactions.


The focus of this work will be on the fine-tuning, evaluation, and multi-faceted deployment of a foundation model for lettuce. An existing DNA foundation model architecture will be leveraged while also incorporating advances due to the rapid evolution of the DNA and other -omic foundation model space. The extensive existing data sets will be leveraged for training, evaluation, validation, and use cases. The project emphasizes reproducible research and open-source software development. The successful candidate will have opportunities to publish both methodological advances in AI and biological discoveries enabled by the models.


Responsibilities

  • Fine-tune a pretrained foundation model using lettuce genome data with applications in crop improvement.

  • Implement appropriate strategies to optimize model performance and benchmark and compare models.

  • Collaborate closely with other project team members who have expertise in lettuce genomic resources, remote sensing, plant physiology, and development to 1) curate training data, including multi-omic and phenotypic data; and 2) generate hypotheses for model training.

  • Supervise undergraduate researchers with training in machine learning.

  • Publish findings in machine learning and computational biology journals.


Requirements

  • Ph.D. in Computer Science, Computational Biology, or a related field

  • Experience programming in Python

  • Demonstrated experience developing, training, or adapting deep learning models

  • An interest and willingness to learn about genome biology, gene function, and regulatory circuits

  • Willingness to collaborate in multi-disciplinary teams

  • Evidence of research productivity through publications in machine learning, computational biology, genomics, or related areas

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