Research Associate - Department of Computer Science and Electrical Engineering

Description

The Lane Department of Computer Science and Electrical Engineering of the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University invites applications for a Research Associate to work with researchers at the AI+Health Engineering Center (AI+HEC). AI+HEC is a cross-cutting collaborative research center involving researchers from different colleges and departments at West Virginia University. The long-term goal of the collaboration is to improve health and well-being by accelerating decision making for intelligent health applications, through the development and application of advanced artificial intelligence (AI) techniques, exploiting the available massive heterogeneous biomedical datasets. Activities under this collaboration will lead to improved workforce development in leading-edge technology areas of data science, AI, and digital health. For more information about AI+HEC, please visit https : / / community.wvu.edu / ~daadjeroh / projects / aihec / .

We are searching for candidates with a strong background in computer vision, deep learning, large language models, generative AI, applied mathematics, bioinformatics, and biomedical image analysis, especially high-resolution histopathology images and cardiac images. The center is focused on developing new algorithms and techniques that use advances in AI, machine learning and large language models to analyze different types of biomedical data. Emphasis is on biomedical images (especially on the analysis of high-resolution pathology images and cardiac images, such as echocardiographs, MRI, CT images, etc.), and on secured and privacy preserving health data analytics.

The Research Associate will conduct research on one or more of the topics above. Together with other researchers at the center, the Research Associate will have the opportunity to determine the specific direction of their research project, depending on their interests, and within the scope of the collaborative projects at the center.

Qualifications

The successful candidate must have a PhD in computer science, electrical engineering, computer engineering, biomedical engineering, applied mathematics, physics or a related field. Familiarity with machine learning / deep learning / data analytics and large language models is a requirement and proficiency in scientific computing using one or more of the major programming languages / environments (python, PyTorch, Matlab, C / C++, or R) is essential.

West Virginia University is the State's comprehensive Land Grant University with an enrollment of about 29,000 students and an R1 Carnegie Classification. Morgantown and its vicinity have a diverse population of about 62,000 residents and are ranked highly among the most livable small cities in the country. The community lies within a high technology corridor that also includes several federal research facilities, as well as industries active in shale gas operations. The city is readily accessible and within driving distance from Pittsburgh, PA and Washington, DC. For more information on WVU and Morgantown, see https : / / www.wvu.edu and http : / / www.morgantownwv.gov. The Lane Department of Computer Science and Electrical Engineering has 30 faculty members and approximately 500 undergraduate and 160 graduate students.

To apply for this position, visit https : / / careers.wvu.edu / career-opportunities. Applicants must submit a cover letter, curriculum vitae, and contact information for two professional references. Only applicants who submit complete documentation will be considered in the review process. For further questions or additional information, please contact Dr. Don Adjeroh via email donald.adjeroh@mail.wvu.edu or Dr. Gianfranco Doretto via email gianfranco.doretto@mail.wvu.edu.

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Join us at West Virginia University, where your work will make a lasting impact. To learn more about WVU, visit wvu.edu.

West Virginia University is an Equal Opportunity Employer. We invite all qualified applicants regardless of race, color, religion, sex, national origin, age, disability, genetic information, or Veteran status.