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The Complex Fluids and Multiphase Transport Laboratory (CFMTL) focuses on advancing fundamental thermal-fluid, interfacial, and data sciences, and applying them to enable efficient energy conversion and storage systems, sustainable manufacturing, and effective thermal management solutions. Current projects range from basic studies of drop dynamics and interfacial transport to additive manufacturing of innovative cooling solutions and design of advanced electrochemical energy storage systems.
The CFMTL has been supported by NSF, NASA, DOE, ARPA-E, AFOSR, ONR, DOEd, EPRI, ACS-PRF, Ben Franklin Tech Partners, States of Ohio and Pennsylvania, and industry. Our integrated experimental, multi-scale modeling, and data-driven research has made significant impacts on energy, water, advanced manufacturing, microelectronics, and materials processing, in particular through collaborations with industrial and academic partners.
Machine Learning
Interpretable Unsupervised Learning
Advanced Metrology
Total Internal Reflection Microscopy
Unique Capabilities/Facilities: GPU AI supercomputing; Multi-scale, multi-physics simulations; Advanced materials printing systems; Complex fluids characterization; High-speed imaging; Thermal imaging; Interferometry; Total internal reflection microscopy; Fluorescence microscopy; Wind tunnel; Thin film coating and solar cell testing devices; Microfluidics and heat transfer testers.
Postdoc Openings (new!):
#1 Machine Learning in Thermal-fluid Sciences: We seek highly motivated individuals to work in a convergent team environment with data scientists and engineers to integrate machine learning, data science, and artificial intelligence methodologies for applications in clean energy, advanced manufacturing, and climate change mitigation. The candidate will also lead projects and supervise graduate students. Prior experience in scientific machine learning is highly desired.
Applicants should have a Ph.D. degree in Mechanical Engineering or a related field and possess excellent communication skills. Prior experience in the following areas is expected:
Multiphase flow and heat/mass transfer
Programming and algorithm development
Mathematical and statistical ML
#2 Experimental Heat Transfer & Multiphase Flows: We seek highly motivated individuals to conduct two-phase heat transfer and interfacial flow experiments, collaborate with modeling experts, and mentor graduate students. Prior experience in advanced imaging techniques and scaling analysis is highly desired. The initial appointment is for one year with the potential to be extended to the second year based on mutual agreement.
Applicants should have a Ph.D. degree in Mechanical Engineering or a related field and possess excellent communication skills. Prior experience in the following areas is expected:
Phase change heat transfer
Multiphase flows and interfacial instabilities
Advanced imaging techniques (e.g., high-speed interferometry, particle image velocimetry)
Thermal-fluid systems design and fabrication
Interested applicants please email a single PDF file containing a detailed CV accompanied by a cover letter and the contact information for two references to Dr. Ying Sun (sunyg@ucmail.uc.edu) with subject line "Postdoc Position". The review of applications will begin immediately. The position will remain open until filled.
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