Postdoctoral Research Associate - Hematology

Our laboratory integrates computational and functional genomic approaches to understand genetic variation and develop new strategies for precision medicine. The successful candidate will work in a highly collaborative environment combining large-scale genomic data, functional genomic screening, genome editing, machine learning, and experimental validation.

A major component of this position will contribute to an ARPA-H-supported program focused on developing personalized genome-editing therapies for rare diseases. The program includes development of AI models for predicting biochemical and cellular genome-editing activity and off-target effects, with experimental data used for model training and prospective validation.

Research Focus

The postdoctoral fellow will develop and apply computational and machine-learning approaches to problems in functional genomics and genome engineering. Research areas may include:

• Developing machine-learning and deep-learning models to predict genome-editing activity, specificity, and off-target effects.
• Integrating genomic sequence with high-throughput biochemical, cellular, and functional genomic data.
• Developing computational methods for analysis and interpretation of genome-editing and functional genomic datasets.
• Applying modern ML approaches to understand the effects of genetic variation and improve genome-engineering strategies.
• Working closely with experimental collaborators to design, evaluate, and validate computational models.

The position will also provide flexibility to pursue broader methodological and biological questions in computational genomics, functional genomics, and AI/ML.

Qualifications

Candidates should have a Ph.D. or equivalent degree in bioinformatics, computational biology, genomics, computer science, statistics, biomedical engineering, data science, or a related quantitative field.

Ideal candidates will have:

• Strong experience in bioinformatics, computational genomics, or related areas.
• Solid 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.

Experience with genome editing, functional genomic screening, sequence-based deep learning, or related technologies is a plus but not required. Candidates with strong computational and machine-learning backgrounds who are interested in learning genome engineering are encouraged to apply.