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Full Time Computer Science Bioinformatics information

What are the key skills and qualifications needed to thrive as a Full Time Computer Science Bioinformatics professional, and why are they important?

To thrive as a Full Time Computer Science Bioinformatics professional, you need a solid background in computer science, biology, and statistics, usually supported by a degree in bioinformatics, computer science, or a related field. Proficiency in programming languages like Python, R, and experience with bioinformatics tools and databases such as BLAST, Bioconductor, and NCBI are typically required. Strong problem-solving abilities, analytical thinking, and effective communication skills help you collaborate with interdisciplinary teams and interpret complex biological data. These skills and qualities are vital for developing innovative computational solutions that advance biological research and healthcare applications.

What types of interdisciplinary collaboration are common in a Full Time Computer Science Bioinformatics role?

In a Full Time Computer Science Bioinformatics position, professionals frequently collaborate with biologists, statisticians, and clinicians to interpret complex biological data. You’ll often work on cross-functional teams, contributing your computational expertise to projects such as genomic analysis, drug discovery, or clinical data interpretation. Effective communication is key, as you'll need to translate technical findings for team members with diverse backgrounds. This collaborative environment not only enriches problem-solving but also broadens your professional network and skill set.

What is the difference between Full Time Computer Science Bioinformatics vs Full Time Data Scientist?

AspectFull Time Computer Science BioinformaticsFull Time Data Scientist
Required CredentialsBachelor's or Master's in Bioinformatics, Computer Science, or related fieldsBachelor's or Master's in Data Science, Statistics, Computer Science
Work EnvironmentResearch labs, healthcare, biotech companiesTech firms, finance, healthcare, consulting
Industry UsageBiotech, pharmaceuticals, academiaTechnology, finance, marketing
Common Search IntentBioinformatics roles, computational biology jobsData analysis, machine learning roles

Full Time Computer Science Bioinformatics focuses on applying computational techniques to biological data, often in healthcare or research settings. Full Time Data Scientist emphasizes analyzing large datasets across various industries. While both roles require programming skills and data analysis expertise, bioinformatics is specialized in biological applications, whereas data science covers broader sectors.

What are full time computer science bioinformatics jobs?

Full time computer science bioinformatics jobs involve using computational and analytical methods to solve biological and biomedical problems. Professionals in these roles develop and apply software tools, algorithms, and databases to analyze biological data, such as DNA sequences, protein structures, and gene expression profiles. These positions typically require strong programming skills, knowledge of biology, and expertise in data analysis. Full time roles may be found in academic research, biotechnology companies, pharmaceutical firms, and healthcare organizations.
What are the most commonly searched types of Computer Science Bioinformatics jobs in Virginia? The most popular types of Computer Science Bioinformatics jobs in Virginia are:
What cities in Virginia are hiring for Full Time Computer Science Bioinformatics jobs? Cities in Virginia with the most Full Time Computer Science Bioinformatics job openings:
Full Time Computer Science Bioinformatics jobs near you
Postdoctoral Research Associate

Postdoctoral Research Associate

University of Virginia

Charlottesville, VA • On-site

Full-time

Re-posted 25 days ago

University Of Virginia rating

7.9

Company rating: 7.9 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

179th of 553 rated colleges and universities

Job description

scholarship during the training period.
The Department of Genome Sciences at the University of Virginia is seeking a highly motivated Postdoctoral Research Associate to join the Miller Lab and contribute to the Leducq COMET Network, an international collaborative effort focused on understanding the mechanisms of vascular calcification and related cardiovascular diseases.
This position is an outstanding opportunity for a computational scientist with strong training in bioinformatics, machine learning, and large-scale genomic data analysis to work at the interface of human genetics, single-cell and spatial multi-omics, cardiovascular biology, and translational medicine. The successful candidate will develop and apply advanced computational approaches to identify disease-associated genes, pathways, cell states, and regulatory mechanisms involved in vascular calcification, atherosclerosis, and broader cardiovascular disease.
Founded in 1819 by Thomas Jefferson, the University of Virginia is renowned for its commitment to advancing knowledge, educating leaders, and cultivating informed citizenship. The Department of Genome Sciences addresses fundamental questions in biology, public health, and medicine by developing and applying state-of-the-art genetic, genomic, computational, and multi-omic approaches to complex human diseases. The Miller Lab focuses on unraveling cardiovascular disease mechanisms by integrating large-scale human genetics, single-cell and spatial multi-omics, functional genomics, and data science approaches.
As part of the Leducq COMET Network, the successful candidate will work in a highly collaborative international environment involving data scientists, genomicists, statisticians, vascular biologists, cardiologists, and other clinical and translational experts. The candidate will contribute to the development of scalable computational pipelines, machine learning workflows, and integrative analyses that enable mechanistic discovery across diverse genomic and multi-omic datasets.
The successful candidate will be expected to:
  • Develop and apply computational methods for the analysis of large-scale genomic, epigenomic, transcriptomic, single-cell, spatial, and multi-omic datasets relevant to vascular calcification and cardiovascular disease.
  • Build, benchmark, and maintain robust bioinformatics pipelines for data processing, quality control, integration, visualization, and reproducible analysis.
  • Use machine learning and statistical approaches to identify disease-associated genes, pathways, regulatory programs, cell states, and molecular mechanisms.
  • Integrate human genetics, functional genomics, and multi-omic datasets to prioritize candidate genes and causal pathways involved in vascular calcification and cardiovascular disease.
  • Work closely with lab members and Leducq COMET Network collaborators to harmonize datasets, refine analysis strategies, and interpret findings in a biological and clinical context.
  • Present progress in weekly group meetings and monthly consortium meetings.
  • Draft manuscripts, contribute to grant applications, and support dissemination of findings through publications and presentations at national and international conferences.
  • Contribute to the training and mentorship of junior lab members, including graduate students, undergraduate researchers, and computational trainees.

Required qualifications:
  • PhD degree in bioinformatics, computational biology, genomics, genetics, biostatistics, statistics, computer science, biomedical engineering, systems biology, or a related quantitative discipline.
  • Strong programming skills in R and Python.
  • Experience working in Linux/Unix environments and using bash, high-performance computing systems, and reproducible computational workflows.
  • Experience analyzing large-scale genomic or multi-omic datasets.
  • Familiarity with workflow management systems such as Nextflow.
  • Strong understanding of statistical analysis, data visualization, and reproducible research practices.
  • Excellent written and oral communication skills.
  • Demonstrated ability to work both independently and as part of a collaborative, cross-functional team.

Preferred qualifications:
  • Experience with single-cell RNA-seq, single-cell ATAC-seq, spatial transcriptomics, epigenomics, proteomics, or other high-dimensional omics datasets.
  • Familiarity with cardiovascular biology, vascular disease, vascular calcification, atherosclerosis, or related disease areas.
  • Experience with machine learning frameworks and workflows, including PyTorch, scikit-learn, and standard supervised and unsupervised learning approaches.
  • Experience developing, containerizing, and documenting reusable computational pipelines.
  • Familiarity with version control, package development, cloud or HPC deployment, and collaborative coding practices.
  • Prior experience contributing to manuscripts, grants, consortium projects, or large collaborative research efforts.

Ideal candidate profile:
The ideal candidate will be a rigorous and creative computational scientist who enjoys developing new analytical approaches while working closely with experimental, clinical, and quantitative collaborators. They will have a strong track record of programming, data
analysis, and problem-solving, along with the ability to communicate complex computational results clearly to both technical and non-technical audiences. A strong team-oriented mindset and enthusiasm for mentoring junior researchers are essential.
This is a restricted position, which is dependent on funding and is contingent upon funding availability. This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding.
This position is based in Charlottesville, VA, and must be performed fully on-site.
Salary range : 50-70k yearly will be commensurate with education and experience
How to Apply
Please apply online, by searching for requisition number R0083987. Complete an application with the following documents:
  • CV (required)
  • Cover letter (required)
  • Academic transcripts (optional)
  • Names of 3 references (required)

Upload all materials into the resume submission field. You can submit multiple documents into this one field or combine them into one PDF. Applications without all required documents will not receive full consideration.
Internal applicants: Search and apply for jobs on the UVA Internal Careers website.
The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment .

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About University of Virginia

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The University of Virginia is distinctive among institutions of higher education. Founded by Thomas Jefferson in 1819, the University sustains the ideal of developing, through education, leaders who are well-prepared to shape the future of the nation.

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10,000+ Employees

Headquarters location

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Year founded

1819

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