Bioinformatics and Computational Biology Professional

Johns Hopkins Applied Physics Laboratory•Laurel, MD

2d•Onsite

About The Position

The Applied Biological Sciences Group (QLA) is seeking a highly motivated scientist or engineer with expertise in molecular biology, bioinformatics, and computational biology to develop technical solutions to address the biological threats facing our nation. This role involves leading and supporting bioinformatics and data science aspects of genomics and systems biology projects, performing and leading analysis using open-source tools and custom pipelines on a High-Performance Computing (HPC) Cluster, and operating in a Linux environment using BASH, Python, and R languages. The position also includes leading and supporting sequence analysis on various data types, identifying and implementing analysis tools for multi-'omics data mining and fusion, and developing novel bioinformatics and computational biology approaches and tools. The role also involves leveraging technical expertise to lead the development of new research proposals. APL works across the US Government applying science and engineering to improve the nation’s ability to safely mitigate biological threats. They partner with the Johns Hopkins enterprise and external collaborators to develop innovative science and technology solutions to biosecurity challenges, focusing on applied research for sensing and detection, threat characterization, and modeling and simulation.

Requirements

A Master's degree in Bioinformatics, Computer Science, Molecular Biology, Applied Mathematics or another field relevant to the duties as described above.
At least one year of professional work experience.
Experience developing and leading bioinformatics and computational biology efforts.
Exceptional interpersonal skills and the ability to clearly communicate complex topics.
Competent understanding of statistics and ability to compare and contrast traditional and biostatistics methods to separate noise or quantify significance within multiple 'omics data sets.
Ability to work across Windows/iOS and Linux systems, and efficiently manage large data sets for a variety of projects.
Familiarity with industry-accepted tools and approaches for biological data analysis including but not limited to metagenomics classification, RNA-Seq, differential expression, etc.
Understanding of assembly and alignment techniques of next-generation sequence fragments and computational resources required to achieve high throughput results.
Ability to model and simulate biological systems in silico using mathematical concepts such as matrices, differential equations, and standard modeling concepts.
Familiarity with algorithms in computer science and applied mathematics commonly used for analyzing complex biological data, such as Expectation-Maximization, Dynamic Programming, K-Nearest-Neighbors (KNN), Support Vector Machines (SVMs), Universal Differential Equations (UDEs), and other AI/ML approaches to data analysis and decision support.
Ability to troubleshoot algorithms and implement software programs for the analysis of such data.
Experience with cluster computing environment and/or resource schedulers for high throughput (parallel) computations, and heuristics for dealing with large data sets; Ability to scale analyses across computational loads.
Demonstrated ability to keep up to date with open-source software and published literature of new tools and examine capabilities and limitations of such tools for sponsor related requirements.
Eligibility to obtain Secret level clearance.
U.S. citizenship.

Nice To Haves

Experience performing (or familiarity with) laboratory sequencing preparations and workflows.
Experience with wet lab molecular biology techniques.
Experience developing and leading multidisciplinary teams and projects.
Experience with BASH, Python, R and Linux environments. Familiarity with Julia, Claude, and Rust.
Ability to utilize and implement Agentic AI tools.
Experience working in Unclassified and Classified areas.
A publication record in computational biology and/or bioinformatics analysis.
A PhD in Bioinformatics, Computer Science, Molecular Biology, Applied Mathematics.

Responsibilities

Lead and support bioinformatics and data science aspects of genomics and systems biology projects.
Perform and lead analysis using open-source tools and custom pipelines on a High-Performance Computing (HPC) Cluster.
Perform and lead bioinformatics tasks operating in a Linux environment, using at a minimum BASH, Python, and R languages.
Lead and support sequence analysis on Illumina, PacBio, and Oxford Nanopore data.
Identify and implement analysis tools for multi-'omics data mining and fusion.
Lead and support novel bioinformatics and computational biology analyses approaches and tool development.
Leverage technical expertise and passion for applied research to lead the development of new proposals for intramural and extramural research aimed at pursuing high-risk, high-reward ideas.