The Samocha Lab in the Center for Genomic Medicine (CGM) at Massachusetts General Hospital and Harvard Medical School, and the Broad Institute is seeking self-motivated and enthusiastic postdoctoral candidates in computational biology, bioinformatics, and/or statistical genetics to join our growing team. The new hire will be appointed as a Research Fellow at Massachusetts General Hospital and Post-Doc at Harvard Medical School. They will also be affiliated with the Medical and Population Genetics program at the Broad Institute of MIT and Harvard.

Advances in genomic technology have allowed the unique opportunity to study patterns of genetic variation in hundreds of thousands of individuals, and have provided insights into the genetic basis of both common and rare disease. However, substantial gaps in our ability to interpret the impact of genetic variation on biological processes have limited our ability to use genetics to guide clinical care. In particular, we still have much to learn about how multiple rare variants may act together to contribute to disease risk.

The successful candidate will work on methods development to extend our ability to interpret the impact of multiple rare variants on disease risk and phenotypic outcomes. They will take a lead role in analyzing large-scale genetic sequencing datasets, including the Genome Aggregation Database (gnomAD) and UK Biobank, and will be responsible for developing and applying novel analytical methods and statistical approaches, with a particular focus on integrating proteomic datasets and machine learning methods.

Candidates must have a doctoral degree (Ph.D. or M.D.), an excellent publication record, and great communication skills. A strong background in bioinformatics and/or experience with analyzing high-dimensional datasets is preferred. Some fluency with genetics concepts and analyses is ideal, as are the ability to work well in a highly collaborative environment.

The Samocha Lab strongly believes that science is a team sport. We are collaborative and approach our work, data, and colleagues with respect, integrity, and enthusiasm. We are based at the CGM at Massachusetts General Hospital and the Program in Medical and Population Genetics at the Broad Institute of MIT and Harvard. The candidate will be embedded into these vibrant research communities, and will work closely with an interdisciplinary team of computational biologists, clinicians, and software developers across multiple laboratories and research initiatives. Working on-site is preferred, but remote work can be arranged if living in New England.

• Working with members of the Samocha Lab, the gnomAD project team, the Hail team, and other local groups to develop, design, and execute statistical analyses and methods to investigate patterns of rare variation
• Conducting detailed analyses of large genomic datasets, including data processing, statistical analysis and interpretation
• Developing novel analytical and statistical approaches
• Writing clear, robust, and re-usable code
• Ensuring the validity, reliability, and integrity of data at all times
• Contributing to the generation of protocols, publications and intellectual property
• Maintaining an up-to-date awareness of scientific literature related to the group’s analysis aims in order to employ the best methods
• Actively participating and presenting in local team meetings to share results, plan projects, and receive feedback
• Communicating work to the wider scientific community through scientific publications, open-source code, and presentations/posters at internal meetings and conferences as appropriate
• Overseeing junior members of the team to hone teaching and mentoring skills 

Essential

• Ph.D. in human genetics/genomics, bioinformatics, computational biology, computer science, mathematics, statistics, or other data sciences
• Knowledge and demonstrated experience in statistical genetics, bioinformatics, or machine learning techniques
• Submitted first-author or joint first-author papers in project relevant fields
• Proficiency in Python, R, Unix, or Hail or other programming languages (e.g. Perl, Java, C++, or Matlab)
• Experience working in HPC and/or cloud-based environments
• Willingness and ability to take ownership of assigned tasks and projects
• Ability to work effectively both independently and as part of a large multidisciplinary team environment
• Good time management and able to adapt to shifting priorities in a dynamic setting
• Excellent communication (both written and oral), presentation, and interpersonal skills

Desirable

• Published (or preprinted) first-author publications
• Knowledge specifically of genomics and its applications to rare disease
• Committed to open and collaborative science
• Background in machine learning or biology is a plus

Not Applicable

Field of Study/Additional Specialized Training:

Not Applicable

Equivalent Experience – please document if educational requirements can be met through equivalent experience:

Not Applicable

Required: Recent PhD graduate

Not Applicable

Essential

• Ph.D. in human genetics/genomics, bioinformatics, computational biology, computer science, mathematics, statistics, or other data sciences
• Knowledge and demonstrated experience in statistical genetics, bioinformatics, or machine learning techniques
• Submitted first-author or joint first-author papers in projeproject-relevantds
• Proficiency in Python, R, Unix, or Hail or other programming languages (e.g. Perl, Java, C++, or Matlab)
• Experience working in HPC and/or cloud-based environments
• Willingness and ability to take ownership of assigned tasks and projects
• Ability to work effectively both independently and as part of a large multidisciplinary team environment
• Good time management and able to adapt to shifting priorities in a dynamic setting
• Excellent communication (both written and oral), presentation, and interpersonal skills

Desirable

• Published (or preprinted) first-author publications
• Knowledge specifically of genomics and its applications to rare disease
• Committed to open and collaborative science
• Background in machine learning or biology is a plus

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