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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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For Postdoc In Topological Data jobs in South Carolina, the most frequently searched job titles are:

What job categories do people searching Postdoc In Topological Data jobs in South Carolina look for?

The top searched job categories for Postdoc In Topological Data jobs in South Carolina are:

Postdoc In Topological Data jobs near you
Infographic showing various Postdoc In Topological Data job openings in South Carolina as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Post Doctoral Fellow - PHS/Data Science

Clemson University

Clemson, SC • On-site

$65K/yr

Full-time, Contractor

Re-posted 7 days ago

Clemson University rating

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Company rating: 7.0 out of 10

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Job description

Description
POSTDOCTORAL FELLOW IN DATA SCIENCE: OUTBREAK ANALYTICS AND DISEASE MODELING
Title: Postdoctoral Fellow
Location: Department of Public Health Sciences (Clemson University's main campus, SC)
Term: renewable 12-month position
Clemson University's Center for Public Health Modeling and Response (PHMR) invites applications for a Postdoctoral Fellowship to join a CDC-funded center grant to create a national network in outbreak analytics and disease modeling. The mission of PHMR is to develop and utilize data-driven approaches to inform clinical and public health decision-making and assist the ability of health organizations and communities to prepare for and respond to, public health threats. The Postdoctoral Fellow will be a part of Clemson's DMA-PRIME division (Disease Modeling and Analytics to inform outbreak Preparedness, Response, Intervention, Mitigation, and Elimination). For this position, we seek an enthusiastic, collaborative researcher with training in data science with an interest in developing and implementing artificial intelligence (AI) and machine learning (ML) techniques to inform and improve outbreak detection, forecasting, and public health response nationwide.
Duties and responsibilities: The postdoctoral fellowship will focus on developing and implementing AI and ML methodology, with the ultimate goal of detecting/ predicting disease hotspots and informing real-time outbreak response efforts. The models and predictions generated will be integrated into health systems across South Carolina to 1) inform and improve strategic delivery of mobile health clinics for infectious disease testing, treatment, and vaccination, 2) inform community awareness, and 3) run statewide disaster simulation scenarios. The postdoc will be expected to lead publications for the models developed and collaborate on (or lead) publications in public health, medical, and policy journals for research related to the implementation of the methods developed.
Supervision
The Postdoctoral Fellow's main appointment will be within the Center for Public Health Modeling and Response, located within the Department of Public Health Sciences. Dr. Rennert will serve as the primary advisor, with Dr. McMahan, Dr. Wu, and Dr. Iuricich as co-advisors. The appointment is funded for 2 years, with future years contingent on funding/performance. Salary is highly competitive with a competitive benefits package and negotiable start date.
Qualifications
Required Qualifications
• A PhD in data science or related fields, with demonstrated knowledge in machine learning, deep learning, or statistical analysis.
• Proficiency in programming languages commonly used in data science and AI, with a strong emphasis on Python. Knowledge of other languages such as R, MATLAB, or C plus plus may be beneficial.
• Proficiency in data preprocessing, cleaning, and feature engineering. Familiarity with tools and libraries for data manipulation, such as pandas and NumPy.
• Expertise in machine learning frameworks and libraries, like TensorFlow, PyTorch, or scikit-learn.
Desirable Qualifications
• Specialized expertise in a particular subfield of AI, such as natural language processing, generative adversarial networks, or transformers.
• Domain-specific knowledge, such as healthcare, medicine
• A history of publishing research findings in conferences, journals, or other relevant platforms.
Application Instructions
Application Instructions: Applicants should submit a cover letter, CV, and contact information for three references through Interfolio: http://apply.interfolio.com/136202
Inquiries should be sent to Dr. Lior Rennert (liorr@clemson.edu) and Dr. Federico Iuricich (fiurici@clemson.edu).
Salary: $65,000
Benefits: https://www.clemson.edu/human-resources/benefits/index.html

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