We are seeking a creative and collaborative computer scientist, statistician, or computational geneticist for a role on our computational genomics team. The team is building a novel platform to identify the molecular circuits underpinning complex human disease, by combining human genetics data with our proprietary single-cell transcriptomics and neuroanatomical tracing data. The group is applying recent breakthroughs in proteomics, next-generation sequencing, network biology, epigenetics, and computational genomics, in order to map the genetic correlates of disease discovered from human genetics studies onto a biological network representing the gut-brain axis. The platform we are building includes proprietary developments in statistical fine-mapping; the application of cell-type-specific epigenetics data; population genetics; the use of predictive deep learning algorithms; and biological networks.
Successful candidates will have a strong background in mathematics and computer science, together with some exposure to biology or genetics, and the personal and technical skills required on a large software engineering effort. Candidates should have a track record of developing sophisticated new mathematical or computational methods, ideally paired with experience working collaboratively on a shared codebase with other scientists or engineers. The ability to transition from academics to industry is a paramount requirement, even though much of our computational genomics work is experimental in nature. The successful candidate will be a team player, whose ideas and contributions are focused on the team's specific objectives.
Opportunities for PhD-level internships on the computational genomics team are also available.
- Work on a small, highly collaborative team to develop the mathematical theory and software that comprise our human genetics analysis platform
- Develop new software (complete with unit and integration tests) in C++ and R, using git for version control and Amazon Web Services (AWS) as the computational infrastructure
- Document new theoretical approaches in LaTeX; contribute to writing manuscripts for internal circulation and publication
- Critically assess current state-of-the-art methods in computational genomics; stay abreast of new developments
- Generate novel ideas to improve our human genetics strategy or the speed with which we implement it
- Participate in code reviews (as both reviewer and author); software deployment and testing; iteration planning and timeline estimation; and software design discussions
Qualifications and Education Requirements
You must have:
- Ph.D. in Mathematics, Statistics, Computer Science, Computational Genomics, or Statistical Genetics
- Raw intelligence and an eagerness to learn
- Experience working collaboratively on a shared scientific codebase
- Deep programming experience in a mathematical or object-oriented language
- Commitment to rigor, in both quantitative analysis and software development
- Excellent communication and interpersonal skills (the role requires communicating about complex biological and computational issues)
- An understanding of major biological and/or genetic concepts