Postdoctoral Associate - Trustworthy Artificial Intelligence at Virginia Polytechnic Institute and State University in Blacksburg, VA

Job description

Job Description The Bradley Department of Electrical and Computer Engineering (ECE) at Virginia Tech invites applications for multiple Postdoctoral Associate positions in trustworthy artificial intelligence (AI). The successful candidates will conduct research aimed at making the decisions and behavior of AI models and agents more transparent, interpretable to humans, safe, and reliable. Research may include developing methods to understand and explain AI decision-making; identifying, evaluating, and mitigating potential failures, vulnerabilities, or risks; and improving the robustness and reliability of AI systems in real-world settings. Working with the faculty supervisor and collaborating researchers, each Postdoctoral Associate will design and execute research studies, disseminate results through peer-reviewed publications and presentations, and develop open-source software, datasets, benchmarks, models, or other research artifacts that benefit the broader research community. The position will also contribute to the development of research proposals and mentor undergraduate students participating in related projects. The appointments provide opportunities for interdisciplinary collaboration and professional development in preparation for careers in academia, industry, government, or the nonprofit sector. Required Qualifications • Ph.D. in computer science, electrical or computer engineering, data science, statistics, applied mathematics, or a closely related field completed by the appointment start date. • PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining. • Record of research in artificial intelligence, machine learning, or a closely related area, documented through the curriculum vitae, resume, cover letter, doctoral dissertation, publications, preprints, or other research products identified in the application materials. • Record of designing and conducting computational or empirical research, including research methodology, implementation, analysis, or documentation, as reflected in research projects, publications, dissertation work, or other scholarly products identified in the application materials. • Programming proficiency relevant to AI or machine-learning research, evidenced by research code, software projects, technical portfolios, repositories, publications, or equivalent work products identified in the application materials. • Record of written and oral technical communication, evidenced by publications, dissertation work, technical reports, conference or seminar presentations, teaching materials, or other examples identified in the application materials. • Record of contributing to both independently led and collaborative research, software, publication, or technical projects, as documented in the curriculum vitae, resume, cover letter, or other application materials. Preferred Qualifications • Research expertise in one or more areas related to trustworthy AI, such as explainable or interpretable AI, AI safety, robustness, reliability, uncertainty quantification, model evaluation, fairness, privacy, security, human-AI interaction, or the evaluation and alignment of AI agents. • Experience with contemporary AI systems, including deep learning, foundation models, large language models, multimodal models, reinforcement learning, or agentic AI systems. • Record of publication in peer-reviewed journals or selective conferences relevant to artificial intelligence, machine learning, human-computer interaction, cybersecurity, or related fields. • Experience developing and maintaining open-source research software, reproducible workflows, datasets, benchmarks, model evaluations, or other community-facing research artifacts. • Experience contributing technical content to grant proposals, sponsored-research reports, or collaborative research plans. • Experience mentoring or supervising undergraduate or graduate researchers. • Experienc