Penn State University is seeking to hire multiple tenure-track or tenured faculty as part of a multi-year cluster hire focused on the intersection of computer science and the life sciences. This effort aims to strengthen the University’s leadership in computational and data-driven approaches to biological, biomedical, and health-related challenges, and to advance the integration of computation into research and education across disciplines. The Department of Computer Science within the College of Engineering, University Park, PA seeks Assistant, Associate, or Full Professors whose research and teaching advance core AI and ML theory, foundation models and generative AI, reinforcement learning and autonomous agents, hardware and systems for AI, edge and federated learning, AI security and privacy, quantum machine learning (QML), robotics, and/or AI-driven discovery in science and engineering (e.g., genomics, bioinformatics, drug discovery, infectious disease modeling, materials, and biomanufacturing). Candidates will possess a PhD in Artificial Intelligence, Computer Science, Computer Engineering, Data Science, Mathematics, Statistics, or a closely related discipline before their appointment start date at Penn State. These faculty will complement a strong and growing cohort of researchers working at the interface of computing, biology, and medicine, including those developing novel computational methods, systems, and tools, as well as those applying data-driven techniques—such as machine learning and AI—to advance discovery in the life sciences. The University has recently expanded its educational offerings in data science, computing, and engineering, providing new opportunities to integrate computational thinking and machine learning into life science curricula. The focus of this search is on candidates with expertise in areas such as computational biology, bioinformatics, biomedical data science, health informatics, computational genomics, systems biology, or related fields that bridge computer science and the life sciences. While experience with modern data-driven or AI-based methods is welcome, it is not required; candidates with strengths in foundational computing, modeling, simulation, systems, or data analysis approaches applied to biological or health domains are strongly encouraged to apply. Successful candidates will join a vibrant, interdisciplinary research community at Penn State and will benefit from extensive collaborative opportunities and resources provided by institutes such as the Institute for Computational and Data Sciences (including advanced computing infrastructure), the Materials Research Institute, and the Clinical and Translational Sciences Institute. Faculty hired through this initiative will be affiliated with the Huck Institutes of the Life Sciences, which serves as a central hub for life sciences research at Penn State. The Huck brings together researchers from diverse fields to address fundamental biological questions and pressing societal challenges. It supports a range of centers and programs, including the Genome Sciences Institute, the Center for Bioinformatics and Computational Biology, and interdisciplinary graduate programs in bioinformatics and genomics, as well as shared core facilities in genomics, proteomics, metabolomics, and related technologies. Successful candidates will be expected to develop internationally recognized research programs, contribute to high-quality graduate and undergraduate education, teach graduate and undergraduate courses, mentor students, and engage in interdisciplinary collaborations across campus and with external partners. They will also contribute to curriculum development that strengthens the integration of computing and life sciences. Academic Ranks Defined: Assistant professors should show early promise in teaching or research through emerging scholarly or professional contributions. Associate professors should have a record of high-quality publications and demonstrate teaching excellence in their field. Full professors should demonstrate a distinguished record of advanced work and leadership, reflecting established excellence in teaching, mentoring, and/or research.
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Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree