Post Doctoral Scholar - Michelmore Lab

University of California; Davis, Northern (CA, KY); Hybrid; USD 65,000 - 90,000; Full time

Job summary

UC Davis Genome Center is seeking a Postdoctoral Scholar to advance a cutting-edge foundation model for lettuce, integrating large-scale genomic, transcriptomic, and phenotypic data for crop improvement.

The role emphasizes reproducible research, open-source software development, and opportunities to publish methodological advances in AI and biology.

Qualifications

• Ph.D. in Computer Science, Computational Biology, or a related field.
• Experience programming in Python.
• Demonstrated experience developing, training, or adapting deep learning models.
• Willingness to learn genome biology, gene function, and regulatory circuits.

Responsibilities

• Fine-tune a pretrained foundation model using lettuce genome data, with applications in crop improvement.
• Implement strategies to optimize model performance and benchmark models.
• Collaborate with team members across lettuce genomic resources, remote sensing, plant physiology, and development to curate training data and generate hypotheses for model training.
• Supervise undergraduate researchers with training in machine learning.
• Publish findings in machine learning and computational biology journals.

Skills

Python programming

Deep learning

Research supervision

Education

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

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