Senior Computational Scientist - Lee Lab

Job Description

About The Jackson Laboratory for Genomic Medicine: Comprising 183,000 square feet of highly advanced, state-of-the-art laboratories and supporting facilities, JAX-GM is conveniently located midway between NYC and Boston in the heart of Connecticut's growing bioscience industry. Our mission at JAX-GM is to discover the complex causes of disease, develop diagnostics and therapeutics, and ultimately empower the global medical community.
JAX-GM is part of an exciting, internationally-recognized research and educational institution with unparalleled genomic resources and research support services. Seeking to advance personalized medicine, we draw on the institution's distinguished history and on the innovative expertise of our growing faculty, who are recruited from among the worlds top scientific labs and institutions.
About the position: A position is available immediately for a Senior Computational Scientist to join the lab of Dr. Charles Lee at the Jackson Laboratory for Genomic Medicine. The research focus of the lab is in the area of structural variation in the human genome and association with human phenotype and disease. This position offers the opportunity to make leading contributions to cutting edge research in human genetics, disease biology, and translational research.

For more on Dr. Charles Lee and his lab's exciting research, visit the Lee Lab online.

KEY RESPONSIBILITIES
Develop new algorithms / pipelines for studying SVs in the human genome.
Data analysis through established algorithms / pipelines to study genome structural variation and its contribution to human disease and genome evolution.

REQUIRED SKILLS
Creativity and eagerness to learn new technologies and science
Enthusiasm for working in a dynamic, high-performance research team environment
Strong communication skills

MINIMUM QUALIFICATIONS
A Ph.D. in bioinformatics, computer science, biostatistics or any relevant field of study
Demonstrable aptitude and expertise in the niche areas of genome variation and/or cancer genomics
Solid publication track record
Proficiency in programming (Python, Java, C++) and statistical analysis (R, MATLAB)
Experience in UNIX-like operating systems and high-performance computing environments
Demonstrable leadership in previous research projects
Experience with applications for external research funding
Track record of developing algorithms and tools in the area of genome variation and/or cancer biology
Familiarity with large datasets from big consortium projects such as TCGA/ICGC/ENCODE and 1000 Genomes
Experience in High Throughput Sequence (HTS) data analysis, microarray data analysis, data integration or evaluation of new analytical tools and technology

MORE ABOUT JAX
JAX began in 1929 with a small group of scientists dedicated to the emerging field of genetics. We now have over 1700 scientists, technicians, and support staff, including over 50 Principle Investigators in five primary disease areas: cancer, reproductive biology, immunology, metabolic processes and neuroscience. Our fundamental contributions to biomedical research include cancer genetics and establishing the mouse as the premier research animal model.
Uncover more of our historic milestones, including the 26 Nobel prizes associated with our research, resources, and education
Read our latest news & insights for a glimpse at how were impacting the future of biomedicine
Follow the progress were making on our quest to improve human health via our recent research highlights


EEO: The Jackson Laboratory provides equal employment opportunities to all employees and applicants for employment in all job classifications without regard to race, color, religion, age, mental disability, physical disability, medical condition, gender, sexual orientation, genetic information, ancestry, marital status, national origin, veteran status, and other classifications protected by applicable state and local non-discrimination laws.

Job Location
Farmington, Connecticut, United States
Position Type
Full-Time/Regular

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