ZipRecruiter

Log In

1

Computational Biology Jobs in Indiana (NOW HIRING)

previous page

Showing results 41-60

All JobsComputational Biology JobsComputational Biology Jobs in Indiana

Computational Biology information

See Indiana salary details

$46.2K

$89.4K

$127K

How much do computational biology jobs pay per year?

As of Sep 7, 2026, the average yearly pay for computational biology in Indiana is $89,436.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,100.00 and $111,300.00 per year, depending on experience, location, and employer.

What is computational biology?

Computational biology is an interdisciplinary field that uses data analysis, mathematical modeling, and computer simulations to understand biological systems and relationships. Researchers in this area develop algorithms and software to analyze large sets of biological data, such as DNA sequences or protein structures. Computational biology plays a crucial role in genomics, drug discovery, systems biology, and personalized medicine, helping scientists make sense of complex biological information.

What are the key skills and qualifications needed to thrive as a computational biologist, and why are they important?

To thrive as a Computational Biologist, you need a strong background in biology, statistics, and computer science, often supported by a relevant degree such as bioinformatics, computational biology, or a related discipline. Familiarity with programming languages like Python or R, experience using bioinformatics tools (e.g., BLAST, Bioconductor), and knowledge of data analysis pipelines are typically required. Strong problem-solving, collaboration, and communication skills help computational biologists interpret complex datasets and work effectively with interdisciplinary teams. These competencies are crucial for extracting meaningful biological insights from large datasets and advancing research in genomics, drug discovery, and personalized medicine.

How do computational biologists typically collaborate with experimental scientists in research projects?

Computational biologists frequently work alongside experimental biologists to interpret data, design experiments, and develop new hypotheses. Collaboration often involves regular meetings to align on research goals, data sharing, and troubleshooting analytical challenges together. Being able to communicate complex computational findings in accessible terms is crucial for ensuring that experimental teams can act on the insights provided. This interdisciplinary teamwork not only enhances research outcomes but also broadens professional skill sets, making the role both dynamic and rewarding.

What is the difference between Computational Biology vs Bioinformatics?

AspectComputational BiologyBioinformatics
Required CredentialsTypically requires a PhD in biology, bioinformatics, or related fieldsOften requires a bachelor's or master's degree in computer science, biology, or bioinformatics
Work EnvironmentResearch labs, academia, biotech companiesResearch labs, healthcare, biotech, and pharmaceutical industries
Industry UsageUsed for modeling biological systems and understanding complex biological dataPrimarily focused on developing algorithms and tools to analyze biological data

Computational Biology and Bioinformatics are closely related fields that often overlap. Computational Biology emphasizes modeling and understanding biological systems through computational methods, often requiring advanced degrees. Bioinformatics focuses on developing tools and algorithms to analyze biological data, typically with a background in computer science or biology. Both roles are vital in research and industry, but they differ in their primary focus and educational requirements.

What can you do with a computational biology degree?

A computational biology degree prepares individuals for roles such as bioinformatics analyst, research scientist, or data scientist in healthcare, biotech, and pharmaceutical industries. Graduates use skills in programming, statistical analysis, and biological data interpretation to develop models, analyze genomic data, and support drug discovery. Proficiency in tools like Python, R, and databases is often required.

What are the most commonly searched types of Computational Biology jobs in Indiana?

The most popular types of Computational Biology jobs in Indiana are:

What are popular job titles related to Computational Biology jobs in Indiana?

For Computational Biology jobs in Indiana, the most frequently searched job titles are:

What job categories do people searching Computational Biology jobs in Indiana look for?

The top searched job categories for Computational Biology jobs in Indiana are:

Computational Biology jobs near you
Infographic showing various Computational Biology job openings in Indiana as of August 2026, with employment types broken down into 64% Full Time, 32% Part Time, 1% Temporary, and 3% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $89,436 per year, or $43 per hour.

Postdoctoral Fellow in Medical and Molecular Genetics

Indiana University

Bloomington, IN • On-site

$45K - $61K/yr

Full-time

Re-posted 23 days ago

Job description

Posting Details
Position Details
Title
Postdoctoral Fellow in Medical and Molecular Genetics
Specific Title
Appointment Type
Postdoctoral Fellow
Department
IUSM - Medical & Molecular Genetics
Campus
IU School of Medicine Indianapolis
Position Summary
Postdoctoral Fellow in Statistical Genetics
The Department of Medical and Molecular Genetics at the Indiana University School of Medicine invites applications for a Postdoctoral Fellow to join the Human Evolutionary and Statistical Genetics (HESG) Lab.
The HESG Lab develops statistical and computational methods to understand human population history and the genetic basis of complex diseases. Our research sits at the intersection of population genetics, statistical genetics, and evolutionary disease genetics, with a particular emphasis on leveraging human genetic diversity to improve genetic discovery, fine-mapping, and biological interpretation.
The laboratory is currently supported by an NIH Pathway to Independence Award (K99/R00) from the National Institute of Mental Health (NIMH) and offers an outstanding environment for developing innovative quantitative methods, establishing independent research directions, and collaborating with investigators across Indiana University School of Medicine, the Broad Institute, and Massachusetts General Hospital.
Research in the HESG Lab spans three major areas. First, we develop population-genetic methods to reconstruct human population history and understand how migration, admixture, demographic change, and natural selection shape genetic diversity. Second, we develop statistical genetics methods for GWAS, fine-mapping, post-GWAS analysis, and disease-gene discovery. Third, we study evolutionary disease genetics by connecting human evolutionary history, genetic variation, and disease biology. Together, these efforts aim to transform human genetic diversity into a powerful resource for understanding complex disease mechanisms and advancing precision medicine.
Responsibilities
The successful candidate will have the opportunity to:
  • Develop novel statistical and computational methods for human genetics and complex disease research.
  • Conduct research in statistical genetics, population genetics, evolutionary disease genetics, and large-scale genomic data analysis.
  • Analyze genomic datasets from population biobanks, sequencing studies, psychiatric genetics studies, and other large-scale human genetics resources.
  • Lead independent research projects while working closely with the PI and multidisciplinary collaborators.
  • Publish research findings in leading peer-reviewed journals and present work at national and international scientific meetings.
  • Contribute to the generation of preliminary data, analytical frameworks, and methodological results that support NIH and foundation grant applications.

Training and Career Development
The HESG Lab is committed to providing a supportive and intellectually stimulating training environment. Postdoctoral fellows will receive close mentorship in developing independent research directions, writing manuscripts, presenting scientific findings, preparing grant and fellowship applications, mentoring junior trainees, and building a long-term career plan.
The position is well-suited for candidates interested in pursuing independent academic careers, research scientist positions, or leadership roles in human genetics, statistical genetics, computational biology, data science, or industry research.
What We Offer
IU School of Medicine is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana.
The successful candidate will join a newly established and rapidly growing research program with strong institutional and NIH support. The laboratory emphasizes methodological innovation, rigorous quantitative thinking, reproducible research, open science, and collaborative team science. Postdoctoral fellows will have opportunities to shape the scientific direction and culture of the lab while developing independent research programs that address fundamental questions in human genetics, population history, psychiatric genetics, and complex disease biology.
Dr. Yuan's prior work provides the foundation for the HESG Lab's current research directions. His statistical genetics work includes the development of SuSiEx, a cross-ancestry fine-mapping framework published in Nature Genetics, as well as contributions to large-scale complex disease genetics studies, including a Crohn's disease exome sequencing study published in Nature Genetics. His population and evolutionary genetics work includes ArchaicSeeker2, published in Nature Communications, for detecting archaic introgression and reconstructing complex introgression histories. Building on this background, the lab continues to develop methods for reconstructing complex population histories, including MultiWaver, MultiWaverX, HierMultiMix, and the recently published HiMWA framework. Together, these studies reflect the lab's long-term goal of developing rigorous statistical methods that connect human evolutionary history, genetic diversity, and complex disease biology.
Basic Qualifications
Required Qualifications
Applicants must have:
  • A Ph.D. in Statistics, Biostatistics, Genetics, Bioinformatics, Computer Science, Mathematics, Data Science, or a related quantitative discipline.
  • Strong quantitative, computational, and programming skills.
  • Excellent written and verbal communication skills.
  • Demonstrated ability to work both independently and collaboratively.
  • Strong interest in developing statistical or computational methods for human genetics.

Preferred Qualifications
Experience in one or more of the following areas is preferred:
  • Statistical genetics
  • Human genetics
  • Population genetics
  • Evolutionary genetics
  • Bayesian statistics
  • Machine learning
  • Large-scale genomic data analysis
  • High-performance computing

Experience in human population genetics, evolutionary genetics, or related quantitative analyses is considered a plus but is not required.
Department Contact for Questions
Application
Applicants should submit:
  • Curriculum Vitae
  • Brief statement of research interests and career goals
  • Contact information for three references

though the Indiana University academic careers portal via this link
https://indiana.peopleadmin.com/postings/33803Applications will be reviewed on a rolling basis until the position is filled.
Kai Yuan, PhD
Assistant Professor
Department of Medical and Molecular Genetics
Center for Computational Biology and Bioinformatics
Indiana University School of Medicine
Principal Investigator
Human Evolutionary and Statistical Genetics (HESG) Lab
Computational Methods for Population History and Complex Disease
Email: yuankai@iu.edu
Additional Qualifications
Special Instructions
Priority Application Review Deadline
Expected Start Date
Posting Number
IUSM-02470-2026

Apply