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One PhD position in multiscale simulation and characterization of polymer composites at Howard University, Washington, DC
The Sustainable Infrastructure, Geotechnics, and Materials (SIGMa) Lab at Howard University invites applicants for a Ph.D. position in multiscale simulation and characterization of polymer composites starting January 2022. Highly motivated individuals with strong computational, machine learning, microscopy and communication skills are encouraged to apply.
Responsibilities
- Fabricating, testing, and characterizing samples
- Developing multiscale computational models
- Publishing papers
- Mentoring undergraduate research assistants
- Participating in collaborative opportunities beyond the project
- Liaising with collaborators
Required qualifications
- A degree in civil/mechanical/materials engineering
- Background in multiscale modeling and simulation of materials (molecular dynamics, finite element, ...) and either or both of the following: microscopy, machine learning and programming (with Python)
- Strong writing skills
- Positive attitude, growth mindset, passion, tenacity, time management skills, willingness to learn new things
Admission Requirements
https://gs.howard.edu/admissions/admission-requirements
Compensation package and contact information
Assistantships include a monthly stipend, tuition support, and health insurance.
Contact information
Interested individuals are encouraged to send their current CV and the contact information of three references to Dr. Hessam Yazdani at hessam.yazdani@howard.edu by June 25, 2021. Please put “Prospective PhD student in SIGMa - 2022” in the subject line to avoid having your email inadvertently overlooked. NOTE: The review of applications could take over a month. Please be patient and do not make follow-ups. You will be notified of the decision.
About the SIGMa Lab
The interdisciplinary research at SIGMa is focused on fostering sustainability and resilience in civil infrastructure by developing high-performance and multifunctional materials, performance monitoring of structures using remote sensing and smart materials, addressing the geotechnical aspects of infrastructure, and incorporating uncertainty analysis and optimization into the analysis and design of interacting geotechnical and structural systems.
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