Description
The Karchin Lab leads computational antigen discovery for a privately funded program developing the next generation of vaccines for the treatment and prevention of pancreatic cancer. The incumbent will develop new computational models and algorithms for predicting which tumor antigens the immune system recognizes, and will use them to discover and prioritize vaccine targets. The available data include tumor sequencing, single-cell T cell receptor data from vaccinated patients, immune assay readouts, and HLA genotypes. Candidate antigens generated computationally are tested experimentally by the program's immunology and clinical collaborators, and the two efforts run as a loop rather than a one-directional handoff.
The position calls for an established computational scientist who already has expertise in neoantigen prioritization, T cell repertoire and specificity analysis, and machine learning on biological sequence data, and who can be productive within the first month. It is not structured as a training position in which those skills would be acquired on the job or carried over from an adjacent field.
Job Responsibilities
- Antigen discovery, 65 percent. Design new computational methods for antigen discovery. Apply these methods to identify and prioritize candidate antigens for vaccine development.
- Scientific collaboration, 15 percent. Work directly with immunology and clinical collaborators to design analyses, interpret assay results, and prioritize candidates for experimental testing.
- Scientific communication, 15 percent. Lead or co-lead manuscripts arising from the work.
- Data governance and compliance, 5 percent. Handle controlled-access human genomic data in accordance with institutional and sponsor requirements.
Qualifications
Minimum qualifications
- PhD or MD/PhD in computational biology, bioinformatics, computer science, statistics, genomics, immunology, or a closely related field, in hand by the start date.
- A record of first-author peer-reviewed publications or preprints demonstrating independent completion of a computational research project.
- Demonstrated ability to communicate scientific results clearly in writing and to non-computational collaborators.
Required technical expertise
- Neoantigen prioritization
- T cell repertoire analysis
- T cell specificity prediction
- Machine learning
- Programming and computing: strong Python, fluency at the Unix command line, version control, and production use of a workflow manager such as Nextflow, Snakemake, or WDL, with experience running analyses at scale on high performance computing clusters or cloud platforms.
Conditions of employment
- The position is on-site in Baltimore and is not eligible for remote work.
- The incumbent must be able to obtain and maintain approval to work with controlled-access human genomic data and protected patient data.
- Recruitment is limited to candidates currently residing in the United States. Johns Hopkins support for a visa transfer or extension may be required for a candidate already in the United States in an appropriate status.
Compensation and appointment terms
- Salary reflects the minimum for this position; the actual offer depends on experience and on whether the position is filled at the postdoctoral or staff scientist level.
- Standard Johns Hopkins benefits apply, including health, dental, and vision insurance, retirement contributions, and paid leave.
- Funding is secured for three years with possible continuation, and appointments are renewed annually.
- Mentoring toward independence and support for fellowship applications are part of the position.