Post Doctoral Fellow - Glenn Department of Civil Engineering
Apply NowCompany: Clemson University
Location: Clemson, SC 29631
Description:
Description
Carry out Computational Fluid Dynamics (CFD) simulations to support the development of numerical wind-resistant design frameworks. Development of Machine Learning (ML) models to handle CFD simulation data. Design and support the construction of a forced vibration rig in the Clemson wind tunnel for testing bridge decks. Carry out static and dynamic wind tunnel tests of sectional models in the Clemson wind tunnel. Develop optimization algorithms for the wind-resistant design of structures.
Qualifications
Education requirement: A Ph.D. degree in civil engineering, mechanical engineering, or other related fields with a focus on computational fluid dynamics, bridge aeroelasticity, or wind engineering. Experience requirement: The candidate should have a strong background in wind engineering with a track record of publications in top journals/conferences in bluff body aerodynamics, aeroelasticity, or wind and bridge engineering. Knowledge, skills, and abilities: CFD simulations, AI techniques, FE modeling, Python, Matlab, Latex. Additional education, experience, and skills that are preferred for this position: Experience with CFD simulations, including 2D and 3D simulations, with both static and dynamic meshes, URANS and LES, different turbulence models, and using open-source software (OpenFoam, Salome, etc.). Experience in applying AI techniques, from surrogates to deep-learning techniques. Experience in developing and parametrizing FE models of civil engineering structures. Experience in applying optimization algorithms. Experience in working with multiple computer languages, including Python, Matlab, bash, etc. Experience in working in parallel computing environments, i.e., HPC clusters.
Application Instructions
Applications, including a cover letter and resume, should be submitted through Interfolio ( http://apply.interfolio.com/149471 ). The position shall remain open until filled.
Carry out Computational Fluid Dynamics (CFD) simulations to support the development of numerical wind-resistant design frameworks. Development of Machine Learning (ML) models to handle CFD simulation data. Design and support the construction of a forced vibration rig in the Clemson wind tunnel for testing bridge decks. Carry out static and dynamic wind tunnel tests of sectional models in the Clemson wind tunnel. Develop optimization algorithms for the wind-resistant design of structures.
Qualifications
Education requirement: A Ph.D. degree in civil engineering, mechanical engineering, or other related fields with a focus on computational fluid dynamics, bridge aeroelasticity, or wind engineering. Experience requirement: The candidate should have a strong background in wind engineering with a track record of publications in top journals/conferences in bluff body aerodynamics, aeroelasticity, or wind and bridge engineering. Knowledge, skills, and abilities: CFD simulations, AI techniques, FE modeling, Python, Matlab, Latex. Additional education, experience, and skills that are preferred for this position: Experience with CFD simulations, including 2D and 3D simulations, with both static and dynamic meshes, URANS and LES, different turbulence models, and using open-source software (OpenFoam, Salome, etc.). Experience in applying AI techniques, from surrogates to deep-learning techniques. Experience in developing and parametrizing FE models of civil engineering structures. Experience in applying optimization algorithms. Experience in working with multiple computer languages, including Python, Matlab, bash, etc. Experience in working in parallel computing environments, i.e., HPC clusters.
Application Instructions
Applications, including a cover letter and resume, should be submitted through Interfolio ( http://apply.interfolio.com/149471 ). The position shall remain open until filled.