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Postdoc In Topological Data Jobs in Virginia (NOW HIRING)

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

Postdoctoral Associate Apply now Back to search results Job no: 537094 Work type: Research Faculty ... Expertise in next generation sequencing approaches and genomic data analysis. Experience with ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

The Postdoctoral Associate is expected to lead research projects from experimental design, data analysis, to presentations in lab meetings, conferences, and seminars, as well as prepare manuscripts ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

They are seeking multiple full-time Postdoctoral Associates to develop agentic AI systems for ... in catalytic systems. • Demonstrated experience with agentic AI, including automated data ...

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Postdoc In Topological Data information

What is a postdoc in topological data?

A Postdoc in Topological Data is a researcher who has completed their PhD and is engaged in advanced research focused on the application of topology—an area of mathematics dealing with spatial properties—to analyze and interpret complex data sets. These positions typically involve both theoretical work and the development of computational tools to extract meaningful patterns from high-dimensional or complex data. Postdocs in this field often collaborate with interdisciplinary teams in mathematics, computer science, and applied domains such as biology or engineering. The role is intended to deepen expertise, publish research, and prepare for academic or research-intensive careers.

What are the key skills and qualifications needed to thrive as a postdoc in topological data?

To thrive as a Postdoc in Topological Data, you need a strong background in mathematics (particularly topology, algebra, and geometry), data analysis, and a PhD in a related field. Familiarity with computational tools like Python, R, MATLAB, and software libraries for topological data analysis (such as GUDHI or Ripser) is typically required. Exceptional problem-solving ability, collaboration, and strong scientific communication skills help distinguish top candidates in interdisciplinary research environments. These skills are crucial for advancing research, publishing high-quality work, and contributing effectively to collaborative scientific projects.

What are some typical collaborative opportunities for a postdoc in topological data within academic or research settings?

As a Postdoc in Topological Data, you can expect to collaborate closely with interdisciplinary teams, including mathematicians, computer scientists, and domain experts from fields like biology or materials science. Collaborative projects often involve developing or applying topological methods to analyze complex datasets, contributing both theoretical insights and computational tools. These interactions may include co-authoring research papers, participating in joint seminars, and working with graduate students. Such collaborations not only broaden your research impact but also help expand your professional network and skill set.

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Infographic showing various Postdoc In Topological Data job openings in Virginia as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 80% Full Time, 14% Part Time, 3% Contract, and 1% Nights. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution.

Biostatistics Postdoctoral Research Associate, Department of Public Health Sciences

Charlottesville, VA

Virginia Department of Human Resource Management
Public Administration • 1 - 5K employees
Learn more about Virginia Department of Human Resource Managementexternal

Full-time

Posted 12 days ago

Virginia Alcoholic Beverage Control Authority rating

5.2

Company rating: 5.2 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

Job description

Biostatistics Postdoctoral Research Associate

The Biostatistics Division in the Department of Public Health Sciences in the School of Medicine at the University of Virginia (UVA) is seeking a highly motivated post-doctoral research associate. The research associate will work with Dr. Hong Zhu and a multidisciplinary research team in development and implementation of novel methods for comparative effectiveness research (CER) using complex observational healthcare data, with application to cancer studies; and collaborate on and support multiple interdisciplinary research projects funded by a multi-institution, NCI-funded, U54 Specialized Program of Research Excellence (SPORE) grant on childhood acute lymphoblastic leukemia, under the supervision of Dr. Hong Zhu, Co-Director of U54 Biostatistics and Data Management Core.

The work for this position will include: Model multilevel survival data in the presence of confounding and missing data, machine learning approaches as appropriate; Develop computing programs and software; Apply new methods to real-world healthcare data; Plan and conduct database development and data management; Design and perform reproducible statistical analyses for interim and final reports; Assist with abstract and manuscript preparation; Develop and implement innovative procedures in data collection, data management, presentation of results, and statistical analyses. The selected candidate will be expected to participate in the development of publications for peer-reviewed journals and grant proposals to funding agencies.

Postdoctoral employment is temporary and is normally limited to an individual who has been awarded a Ph.D. or equivalent doctorate within the previous five years and who will be involved in full-time research or scholarship at the University. Employment as a Postdoctoral Research Associate is viewed as training and is preparatory for a full-time academic or research career, is supervised by a senior scholar, and allows the appointee to publish the results of his/her research or scholarship during the training period.

Minimum Qualifications:

Qualified candidates must have a PhD in biostatistics, statistics, or a related quantitative field, by the appointment start date.

Preferred Qualifications:

Strong background and experience in survival analysis, missing data methods, causal inference, machine learning, and clinical biostatistics are preferred, including proficiency in R, SAS/STATA, Python, and/or Markdown. Excellent written and verbal communication skills are required. This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings, and programs. Salary will be commensurate with education and experience. This is an exempt-level, benefited position. 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.

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