Postdoctoral Research Associate - Hematology
About The Position
The Cheng Laboratory at St. Jude Children’s Research Hospital is seeking a highly motivated Postdoctoral Research Associate with expertise in bioinformatics, computational genomics, and machine learning. The role develops computational approaches at the intersection of functional genomics, genome engineering, and therapeutic development. The laboratory combines computational and functional genomic approaches to understand genetic variation and develop strategies for precision medicine in a highly collaborative environment involving large-scale genomic data, functional genomic screening, genome editing, machine learning, and experimental validation. A major component of the position contributes to an ARPA-H–supported program to develop personalized genome-editing therapies for rare diseases. This includes developing AI models to predict biochemical and cellular genome-editing activity and off-target effects, using experimental data for model training and prospective validation. The postdoctoral fellow will develop and apply computational and machine-learning approaches in functional genomics and genome engineering, with flexibility to pursue broader methodological and biological questions in computational genomics, functional genomics, and AI/ML.
Requirements
- Ph.D. or equivalent degree in bioinformatics, computational biology, genomics, computer science, statistics, biomedical engineering, data science, or a related quantitative field.
- Strong experience in bioinformatics, computational genomics, or a related area.
- Experience developing and evaluating machine-learning or statistical models.
- Strong programming skills in Python and/or R.
- Experience analyzing genomic, sequencing, or other large-scale biological datasets.
- Strong quantitative and problem-solving skills.
- Ability and enthusiasm to work in a collaborative, multidisciplinary research environment.
- Strong scientific communication skills.
Nice To Haves
- Experience with genome editing, functional genomic screening, sequence-based deep learning, or related technologies; this experience is a plus but not required.
Responsibilities
- Develop machine-learning and deep-learning models to predict genome-editing activity, specificity, and off-target effects.
- Integrate genomic sequence data with high-throughput biochemical, cellular, and functional genomic data.
- Develop computational methods to analyze and interpret genome-editing and functional genomic datasets.
- Apply modern machine-learning approaches to understand the effects of genetic variation and improve genome-engineering strategies.
- Collaborate with experimental researchers to design, evaluate, and validate computational models.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
