Senior and Junior Computational Biologists

VA Boston Healthcare System
Centralized Interactive Phenomics Resource (CIPHER)
United States Massachusetts Boston


New positions for computational biologists are open at VA Boston Healthcare System (VABHS), New England's premier referral center for Veterans' healthcare. The potential appointees will work on the uses of human genetics to identify and validate novel drug-targets at scale using data from the Million Veteran Program, other major biobanks (e.g. UK-Biobank and All of US) and all publicly available genomic resources. The human genetics resources include common and rare variants and large scale sources of quantitative trait loci available to the group and use a diverse set of analytical strategies including causal inference and machine learning methods. The potential appointees will also play an important role in the provision of results through a knowledge-sharing platform known as Centralized Interactive Phenomics Resource (CIPHER).

Computational Biologist Role: As a Computational Biologist you will provide computational support to all projects and help with data analysis. You will also develop and apply a range of computational tools to large datasets of genetics and genomics, write manuscripts for publication, and manage and develop code and generate data within the group. As a Computational Biologist you will help develop and apply computational pipelines to identify and prioritize drug targets.


Essential Skills for both roles:
• Senior Computational Biologist
• PhD in Computational Biology, or a closely related discipline (Senior) or MSc with two years of experience (Junior).
• Advanced level programming skills suitable for genetic and computational analyses, such as Python or R.
• Ability to collaboratively develop software with effective communication.
• Ability to utilize a command-line interface.
• Experience working in a HPC environment (SLURM workload manager a plus).
• Experience using version-control software.
• Experience with visualizing high dimensional data is a plus.
• Experience using machine learning methods is a plus.
• Previous experience in working with large-scale genetic datasets.
• Good understanding of statistical methods of complex and/or rare disease genetics such as GWAS, whole exome/genome sequencing analyses, Mendelian Randomization, PheWAS, identification and quality control of rare variants.
• Ability to work to tight timelines.
• Demonstrable project management and organizational skills.
• Fluent in written and spoken English.
• Ability to communicate ideas and results effectively.
• Ability to work independently and organize own workload

This is an opportunity to work across a range of analyses including:
• Multi-ancestry GWAS-by-PheWAS
• Rare and loss-of-function (LoF) variants
• Mendelian Randomization results using diverse sources of QTLs
• Machine Learning approaches (**experience in this area is a plus)

You will work with an existing inter-disciplinary team as well as national and international collaborators with expertise in medicine, chemistry, causal inference, and functional and statistical genomics. You will be supported in your personal and professional development and have the opportunity to conduct peer-reviewed publications using genetics and genomics approaches to guide drug discovery and present them at national and international conferences.

Applications will be reviewed on an ongoing basis, and the role may close early when a successful appointment is made.

Please apply with your CV and a Cover letter outlining how you meet the criteria set out above and in the job description. Please send all of the above to Constance Hoag at

Start date

As soon as possible

How to Apply

Please apply with your CV and a Cover letter outlining how you meet the criteria set out above and in the job description. Please send all of the above to Constance Hoag at


Constance Hoag