Openings

The Applied Artificial Intelligence and Sensing Laboratory (AIS-Lab) at the University of Dayton invites applications for one postdoctoral researcher to join our vibrant and interdisciplinary research group. Successful candidates will work in the broad domains of remote sensing, multimodality data analysis, database development, AI technologies, numerical simulation, and their applications to multidisciplinary engineering challenges. The work scope for current openings is interdisciplinary by nature, spanning over multimodal sensing, audio and acoustic technologies, computer vision, sensing and data fusion, SAR applications, Multiphysics and multiscale modeling, and risk-informed decision making. The primary research project is sponsored by the USDOT, Ohio Bureau of Workers’ Compensation (BWC), and industry sponsors like Saudi Aramco. Other projects may include digital twin–based intelligent infrastructure maintenance and risk assessment, Bayesian uncertainty analysis, photogrammetry and structured light 3D scanning techniques for multiscale characterization, SAR-based infrastructure monitoring, AI-driven material property evaluation, and advanced multi-physics modeling. The research programs are open to the applicants and can be discussed with the PI.

The mentor of the Postdoc will be PI Dr. Hui Wang. PI Dr. Wang is currently an Associate professor with the Department of Civil and Environmental Engineering, the University of Dayton. Before his faculty appointment at UD, Dr. Wang has several years of research experience in machine learning and computational geosciences at the RWTH Aachen University in Germany. His research focuses on the opportunities in the multidisciplinary fields spanning machine learning, corrosion engineering, geotechnical/geological subsurface modeling, smart infrastructure, and reliability & risk assessment of infrastructure systems. His work receives ongoing support from the USDOT and industry partners. He is a member of ISSMGE TC304 (Engineering Practice of Risk Assessment and Management), and ASCE\Geo-Institute Technical committee: Risk Assessment and Management. He is a reviewer for all major international journals of geotechnical engineering, civil and infrastructure engineering, and engineering geology. He is also on the editorial board of the Journal of Pipeline Science and Engineering, Georisk, and Geodata and AI. He is invited as one of the ISSMGE Bright Spark Lecturers.

AIS Lab has diverse collaborations with internal and external partners, examples including the School of Engineering's research labs and centers such as Intelligent Signal Systems Lab, Greg and Annie Stevens Intelligent Infrastructure Engineering Lab, Applied Sensing Lab, the University’s Hanley Sustainability Institute and the University of Dayton Research Institute (UDRI), as well as several research labs at Texas A&M University, University of Georgia, Rutgers University, University of Cincinnati, and Missouri S&T.

The University of Dayton School of Engineering offers a dynamic and interdisciplinary Ph.D. in engineering program designed to cultivate the next generation of engineering scholars and innovators. With a strong foundation in applied research, industry collaboration, and experiential learning, UD’s doctoral programs empower students to solve complex, real-world challenges with creativity and technical excellence. Ph.D. students work closely with dedicated faculty mentors across departments—including Civil and Environmental Engineering, Electrical and Computer Engineering, Mechanical Engineering, and more—engaging in cutting-edge research funded by federal agencies (e.g., NSF, DoD, DOE, USDOT) and industry leaders.

The School is known for its state-of-the-art research infrastructure, including the University of Dayton Research Institute (UDRI), numerous faculty-led laboratories, and partnerships with nationally renowned institutions such as the Air Force Research Laboratory at Wright-Patterson Air Force Base. Students benefit from a collaborative campus culture, personalized academic support, and a vibrant research ecosystem that spans intelligent sensing, AI and data science, cyber-physical systems, aerospace, sustainability, and advanced materials.

Located in Dayton, Ohio—a recognized hub for innovation in engineering and aviation—the University offers doctoral students not only a rigorous academic experience, but also a welcoming community and outstanding quality of life. Graduates of the program go on to impactful careers in academia, national labs, industry R&D, and public service.

Minimum Qualifications

·         PhD at the time of hiring in civil/electrical, computer or mechanical engineering, computer science, data science, robotics, construction engineering and management, or GIS.

·         Demonstrated publication records

·         Effective written communication skills

Preferred Qualifications

While not everyone may meet all preferred qualifications, the ideal candidate will bring many of the following:

●        Prior research experience in topics related to multimodal sensing, acoustic and vision signal processing, numerical simulation, sensor fusion, or real-time hazard detection systems.

●        Research and/or industry experience involving technologies such as microphone arrays, thermal and RGB imaging systems, photogrammetry or structure-from-motion for 3D scene reconstruction, multi-physics numerical modeling, SAR techniques, digital twin technologies, and AI-based hazard detection.

●        Experience in acoustic localization techniques and machine learning approaches for sound source classification and hazard identification. Familiarity with designing and integrating sensor networks, performing controlled laboratory experiments, or field testing of sensing systems.

●        Effective interpersonal communication skills.

Assistantship package

Competitive salary for Postdoc researcher in the Ohio State. For qualified applicants, the details can be discussed with the PI in ad hoc cases.

Special Instructions to Applicants

Please send the following documents directly to Dr. Hui Wang (hwang12@udayton.edu), Dr. Yusheng(Bear) Jiang (bjiang1@udayton.edu), and Dr. Sreelakshmi Sreeharan (sreeharans1@udayton.edu)

●        A cover letter that describes how you meet all minimum and any preferred qualifications.

●        A curriculum vitae

●        Contact information for three references who will be contacted later in the process

●        A 2-page research statement describing a vision of your research and how you can contribute to the lab focusing on the topics mentioned above.

●        Unofficial transcript.

The Appliead Artificial Intelligence and Sensing Laboratory (AIS-Lab) at the University of Dayton invites applications for one PhD student to join our vibrant and interdisciplinary research group. Successful candidates will work in the broad domains of remote sensing, multimodality data analysis, database development, AI technologies, numerical simulation, and their applications to multidisciplinary engineering challenges. The work scope for current openings is interdisciplinary by nature, spanning over multimodal sensing, audio and acoustic technologies, computer vision, sensing and data fusion, SAR applications, Multiphysics and multiscale modeling, and risk-informed decision making. The primary research project is sponsored by the USDOT, Ohio Bureau of Workers’ Compensation (BWC), and industry sponsors like Saudi Aramco. Other projects may include digital twin–based intelligent infrastructure maintenance and risk assessment, Bayesian uncertainty analysis, photogrammetry and structured light 3D scanning techniques for multiscale characterization, SAR-based infrastructure monitoring, AI-driven material property evaluation, and advanced multi-physics modeling. The research programs are open to the applicants and can be discussed with the PI.

The PhD student will be co-advised by PI Dr. Wang and co-PIs Dr. Yusheng (Bear) Jiang, and Dr. Sreelakshmi Sreeharan. PI Dr. Wang is currently an Associate professor with the Department of Civil and Environmental Engineering, the University of Dayton. Before his faculty appointment at UD, Dr. Wang has several years of research experience in machine learning and computational geosciences at the RWTH Aachen University in Germany. His research focuses on the opportunities in the multidisciplinary fields spanning machine learning, corrosion engineering, geotechnical/geological subsurface modeling, smart infrastructure, and reliability & risk assessment of infrastructure systems. His work receives ongoing support from the USDOT and industry partners. He is a member of ISSMGE TC304 (Engineering Practice of Risk Assessment and Management), and ASCE\Geo-Institute Technical committee: Risk Assessment and Management. He is a reviewer for all major international journals of geotechnical engineering, civil and infrastructure engineering, and engineering geology. He is also on the editorial board of the Journal of Pipeline Science and Engineering, Georisk, and Geodata and AI. He is invited as one of the ISSMGE Bright Spark Lecturers.

Dr. Jiang is currently a Postdoctoral to Tenure-Track Research Fellow with the Department of Civil and Environmental Engineering. Before joining UD. Dr. Jiang earned his Ph.D. degree and subsequently worked as a postdoctoral researcher at Case Western Reserve University, where he conducted pioneering research in multi-physics and multi-scale modeling of geo-infrastructure and its environmental interactions in cold regions. His research focuses on integrating advanced computational modeling, remote sensing technologies, and machine learning to address challenges in infrastructure sustainability, construction safety, and hazard detection. His work has been supported by the National Cooperative Highway Research Program (NCHRP), the National Science Foundation (NSF), and industry partners. Dr. Jiang has authored multiple peer-reviewed journal articles and conference papers in the fields of numerical modeling, remote sensing, and civil infrastructure engineering. He also serves as a reviewer for journals covering geotechnical engineering, computational mechanics, and sensing technologies.

Dr. Sreelakshmi Sreeharan is currently the Postdoctoral Researcher at the University of Dayton, bringing a robust combination of interdisciplinary expertise in physics, engineering, and computational methodologies. Her work bridges electrical engineering, computer vision, and applied machine learning, with a strong emphasis on Bayesian modeling, uncertainty quantification, and intelligent sensing systems. Her research has supported multiple federally funded initiatives, contributing to advancements in critical infrastructure monitoring, corrosion risk assessment, and 3D infrastructure inspection using structured light techniques. Notably, her contributions to pipeline integrity management and predictive modeling under U.S. DOT PHMSA projects and Saudi Aramco highlight her ability to integrate theoretical models with real-world applications, ensuring reliability and accuracy in safety-critical infrastructure systems.

AIS Lab has diverse collaborations with internal and external partners, examples including the School of Engineering's research labs and centers such as Intelligent Signal Systems Lab, Greg and Annie Stevens Intelligent Infrastructure Engineering Lab, Applied Sensing Lab, the University’s Hanley Sustainability Institute and the University of Dayton Research Institute (UDRI), as well as several research labs at Texas A&M University, University of Georgia, Rutgers University, University of Cincinnati, and Missouri S&T.

The University of Dayton School of Engineering offers a dynamic and interdisciplinary Ph.D. in engineering program designed to cultivate the next generation of engineering scholars and innovators. With a strong foundation in applied research, industry collaboration, and experiential learning, UD’s doctoral programs empower students to solve complex, real-world challenges with creativity and technical excellence. Ph.D. students work closely with dedicated faculty mentors across departments—including Civil and Environmental Engineering, Electrical and Computer Engineering, Mechanical Engineering, and more—engaging in cutting-edge research funded by federal agencies (e.g., NSF, DoD, DOE, USDOT) and industry leaders.

The School is known for its state-of-the-art research infrastructure, including the University of Dayton Research Institute (UDRI), numerous faculty-led laboratories, and partnerships with nationally renowned institutions such as the Air Force Research Laboratory at Wright-Patterson Air Force Base. Students benefit from a collaborative campus culture, personalized academic support, and a vibrant research ecosystem that spans intelligent sensing, AI and data science, cyber-physical systems, aerospace, sustainability, and advanced materials.

Located in Dayton, Ohio—a recognized hub for innovation in engineering and aviation—the University offers doctoral students not only a rigorous academic experience, but also a welcoming community and outstanding quality of life. Graduates of the program go on to impactful careers in academia, national labs, industry R&D, and public service.

Minimum Qualifications

●        B.S. at the time of admission in civil/electrical, computer or mechanical engineering, computer science, data science, robotics, construction engineering and management, or GIS is highly preferred. Degrees in engineering technology, environmental engineering, engineering physics, applied mathematical sciences, industrial engineering, or related fields are also acceptable.

●        Effective written communication skills.

Preferred Qualifications

While not everyone may meet all preferred qualifications, the ideal candidate will bring many of the following:

●        Prior research experience in topics related to multimodal sensing, acoustic and vision signal processing, numerical simulation, sensor fusion, or real-time hazard detection systems.

●        Research and/or industry experience involving technologies such as microphone arrays, thermal and RGB imaging systems, photogrammetry or structure-from-motion for 3D scene reconstruction, multi-physics numerical modeling, SAR techniques, digital twin technologies, and AI-based hazard detection.

●        Experience in acoustic localization techniques and machine learning approaches for sound source classification and hazard identification. Familiarity with designing and integrating sensor networks, performing controlled laboratory experiments, or field testing of sensing systems.

●        Effective interpersonal communication skills.

Assistantship package

Competitive stipend for PhD students, full tuition waiver, full travel fund for conferences and data collection. For qualified applicants, the details can be discussed with the PI in ad hoc cases.

Special Instructions to Applicants

Please send the following documents directly to Dr. Hui Wang (hwang12@udayton.edu), Dr. Yusheng(Bear) Jiang (bjiang1@udayton.edu), and Dr. Sreelakshmi Sreeharan (sreeharans1@udayton.edu)

●        A cover letter that describes how you meet all minimum and any preferred qualifications.

●        A curriculum vitae

●        Contact information for three references who will be contacted later in the process

●        A 2-page research statement describing a vision of your research and how you can contribute to the lab focusing on the topics mentioned above.

●        Unofficial transcript.