Research Assistant Professor for Advanced Construction Materials and Digital Construction

Role details

The University of Tennessee, Knoxville, invites applications for a Research Assistant Professor contributing to the Buildings Convergent Research Initiative (CRI) of the University of Tennessee–Oak Ridge Innovation Institute (UT–ORII). The position will advance construction materials and manufacturing approaches that support affordable, energy-efficient, resilient, and adaptable buildings. We seek an emerging scholar with expertise in concrete, cementitious systems, or other performative construction materials, and research interests connecting materials to digital construction and the adaptive built environment.

Connections with artificial intelligence (AI), machine learning, and advanced computation are encouraged. Experimental, computational, and integrated research approaches are welcome. Research Emphasis Candidates should demonstrate depth in one or more of the following connected areas and a clear vision for contributing to the Buildings CRI. Advanced and performative construction materials. Concrete, alternative binders, composites, and multifunctional materials with engineered mechanical, thermal, environmental, or responsive behavior.

Relevant topics include composition–processing–structure–property relationships, durability, resource efficiency, and performance over the material life cycle. Digital construction and advanced manufacturing. Concrete 3D printing, robotic fabrication, and automated construction, including printable mixture design, rheology, buildability, interlayer behavior, process monitoring, and relationships between fabrication parameters and component performance. Adaptive built environments. Materials, components, and building envelopes that respond to changing environmental conditions or operational needs.

Examples include thermal buffering, integrated sensing, self-healing or other responsive functions, and designs that improve adaptability, energy performance, and resilience. Connections with AI and computation. AI-assisted materials discovery, inverse design, process optimization, computer vision, digital twins, or physics-informed modeling that connects material behavior, construction processes, and building performance. Research Environment The Buildings CRI brings together UT and Oak Ridge National Laboratory (ORNL) expertise in advanced materials, robotics, automation, and construction processes.

The successful candidate will collaborate across disciplines and engage with university, national laboratory, and industry partners to connect fundamental research with scalable construction and building applications. Learn more: UT–ORII Buildings CRI Responsibilities Develop an original research direction aligned with the Buildings CRI and contribute to collaborative projects spanning materials, fabrication, and building performance. Lead research tasks from study design and execution through analysis, publication, and dissemination; deliver high-quality results and meet project milestones.

Prepare and contribute to competitive proposals for external research funding in collaboration with UT, ORNL, and other partners. Mentor graduate and undergraduate researchers, contribute to team development, and promote rigorous, reproducible, and safe research practices. Build collaborations that support prototype development, validation, technology transfer, and translation of research findings into practical construction applications.

Required Qualifications

An earned PhD by the appointment start date in civil and environmental engineering, materials science and engineering, or a closely related field. Demonstrated research expertise relevant to construction materials, digital construction, or adaptive building technologies, supported by scholarly accomplishments appropriate to career stage. Evidence of potential to develop an impactful research program and contribute to externally funded, interdisciplinary research. Strong written and oral communication skills and the ability to work effectively with researchers from different disciplines.

Preferred Qualifications

Experience connecting material formulation, characterization, or modeling with manufacturing processes and component or building performance. Experience in concrete 3D printing, robotic fabrication, multifunctional materials, or adaptive building systems. Experience applying AI, machine learning, or advanced computational methods, or a compelling plan to establish these connections through collaboration. Contributions to proposal development, student mentorship, collaborative research, or partnerships with industry or national laboratories. Additional Information This is a 12-month, non-tenure track position. The appointment will be with the University of Tennessee, Knoxville.

The primary work location for this position is on the University of Tennessee campus in Knoxville and may include visits to collaborator sites on the campuses of ORNL and UTK. The position will be implemented as an initial three-year appointment with full salary support for the first year, followed by a gradual increase in expected external salary funding in subsequent years. The applicant will be expected to recruit graduate students by year three. A competitive startup package including funds for graduate students and equipment will be offered. Salary and title/rank will be determined commensurate with experience.

Benefits will be typical of exempt employees at the University of Tennessee. The candidate must be eligible for initial clearance at Oak Ridge National Laboratory.