The Jackson Laboratory (JAX) is seeking an Associate Computational Scientist to apply and develop advanced artificial intelligence and machine learning methods to analyze imaging and multi-omics data across diverse biomedical research areas, including cancer, aging, longevity, and neurodegenerative disorders.
Requirements
- Proficiency in Python required.
- Familiarity with Numpy and Pandas; R experience is a plus.
- Proven experience in image analysis.
- Additional experience in MRI and brain atlas is a plus.
- Familiarity with deep learning frameworks (TensorFlow/Keras and/or PyTorch); experience with generative AI is a plus.
- Hands-on experience with bioinformatics tools and single-cell RNA-seq analysis.
- Demonstrated knowledge of statistical tests and machine learning algorithms, including linear mixed models, SVM, random forest, and gradient boosting.
- Solid knowledge of Unix OS and version control systems.
Responsibilities
- Process and harmonize multi-omics and imaging datasets; analyze them using existing bioinformatics or AI/ML tools and adapt methods as needed.
- Develop new approaches for spatial omics analysis and multimodal data integration across species and cell models under supervision.
- Lead or contribute to multiple grant-funded projects, ensuring progress toward milestones with minimal supervision.
- Prepare reports and manuscripts for projects you lead and contribute to grant applications.
Other
- Ph.D. in Bioinformatics, Computer Science, Biostatistics, Machine Learning, or a related field.
- 2–5 years of experience in bioinformatics, computational biology, or related research, excluding time spent earning a Ph.D.
- Working knowledge of cancer, aging, longevity, or neurodegenerative disorders preferred.
- Ability to effectively present research findings at conferences and workshops.
- Proven track record of peer-reviewed publications and ability to contribute to grant proposals.
