The Department of Electrical and Computer Engineering (ECE) at Carnegie Mellon University ranked among the top programs in the United States, both at the undergraduate and graduate levels. We are an extremely collaborative department with ties to several multidisciplinary institutes and centers. We collaborate with colleagues around the world through a number of research and educational programs. We have world-class experimental and computing infrastructure, including state-of-the-art nanofabrication facilities.
Carnegie Mellon University is inviting applications for a fully-funded postdoctoral research associate position at CMU on the topic of Model and Data Sharing Regarding Network Security Incidents. The postdoctoral fellow will be advised by Profs. Giulia Fanti (ECE) and Vyas Sekar (ECE) and will collaborate with researchers from Duke University and University of Chicago.
This multi-institution research effort involves exploring new models and ML-based techniques for sharing more fine-grained data about network security incidents. Today, enterprises rely primarily on their own network logs to identify if they are under attack. However, any single organization’s data may be insufficient to detect novel attacks or anomalous traffic patterns. Hence, there is a need to share data across organizations to react more quickly to emerging threats. However, today, data sharing efforts are limited in scope and retroactive, due largely to privacy concerns about sharing potentially-sensitive datasets. In this project, the postdoctoral researcher will explore new models of sharing privacy-preserving network data.
Core responsibilities will include:
- Conduct and lead research projects, as well as forge productive collaborations and partnerships with industry stakeholders.
- Leverage recent developments in privacy-preserving machine learning, networking, and security, to develop tools and algorithms that will lead to the design, implementation, and operation of novel security tools and procedures that can enhance financial security and inclusion in Africa.
- Candidates must hold (or be close to completing) a Ph.D. in Electrical Engineering, Computer Science, Machine Learning, or a related field.
- PhD holders with a strong publication record, a background in machine learning, security, and/or networking are encouraged to apply.
CMU is committed to the professional development of its members, making this position a valuable preparation for those interested in academic, industrial, or entrepreneurial careers. The position has no mandatory teaching or administrative duties, although successful candidates will be encouraged to co-teach with faculty at CMU.
The ideal start date is May 2021 or earlier. Applications will be accepted on a rolling basis. The position is initially for 12 months with the possibility of renewal. Compensation will be commensurate with relevant experience. Candidates should send a CV, a statement of research experience and interests, expected date of availability, and the contact information for three references to email@example.com. Application review will proceed until the position is filled.
Note: Due to COVID-19, the early portions of the appointment will be fully remote. However, once it is safe to do so, applicants must be willing to relocate to Pittsburgh, PA.
Please visit “Why Carnegie Mellon” to learn more about becoming part of an institution inspiring innovations that change the world.
A listing of employee benefits is available at: www.cmu.edu/jobs/benefits-at-a-glance/.
Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.
Statement of Assurance: https://www.cmu.edu/policies/administrative-and-governance/statement-of-assurance.html.
Job Function: Research
Primary Location: United States-Pennsylvania-Pittsburgh
Time Type: Full Time
Minimum Education Level: Doctorate
Carnegie Mellon University does not discriminate and Carnegie Mellon University is required not to discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex or handicap in violation of Title VI of the Civil Rights Act of 1964, Title IX of the Educational Amendments of 1972 and Section 504 of the Rehabilitation Act of 1973 or other federal, state, or local laws or executive orders.
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