What are the responsibilities and job description for the Postdoctoral Research Scientist (Computational Mechanics and Machine Learning for Cardiovascular Devices) position at Baylor Careers?
A humble college founded before Texas was a state, Baylor now stands as one of the premier institutions of higher education in the world. Baylor has continued to invest in research and doctoral education, both solidifying and elevating its unique status as a preeminent Christian research university. Baylor is among 187 R1 institutions, just 52 of which are private universities.
Since 2011, Baylor has been named as one of the nation’s “Great Colleges to Work For”. Great Colleges to Work For® is one of the country’s largest and most respected workplace recognition programs. Each year, Great Colleges recognizes the colleges and universities that get top employee ratings regarding workplace practices and policies across 10 areas. This recognition is based on faculty and staff responses to a related annual survey. The survey, conducted by ModernThink, is a way for colleges and universities to gain a deeper understanding of how their employees experience and view various aspects of the institution. This continued recognition further affirms Baylor as a rewarding place to build a career.
What We Are Looking For
Professor Brent Craven in the Department of Mechanical Engineering at Baylor University is seeking a highly motivated Postdoctoral Research Associate with a strong background in cardiovascular computational mechanics and machine learning. The successful candidate will lead research efforts in two primary areas:
- Computational Mechanics: Applying advanced finite element analysis (FEA) and fluid-structure interaction (FSI) models of the cardiovascular system and medical devices
- Machine Learning (ML): Developing and applying ML-based surrogate modeling for accelerating predictions from cardiovascular computational mechanics simulations
This position offers a unique opportunity to work at the leading edge of computational mechanics and machine learning applied to cardiovascular medical devices.
A Doctoral degree is required.
Preferred qualifications include:
- Ph.D. in Mechanical Engineering, Biomedical Engineering, Computational Science, or a related field
- Expertise in computational mechanics modeling using commercial (e.g., Abaqus) and/or open-source software (e.g., OpenFOAM, IBAMR)
- Demonstrated experience with machine learning
- Strong programming proficiency in Python and C
- Substantial experience working in Linux environments and utilizing High-Performance Computing (HPC) resources
What You Will Do
- Applying advanced finite element analysis (FEA) and fluid-structure interaction (FSI) models of the cardiovascular system and medical devices
- Developing and applying ML-based surrogate modeling for accelerating predictions from cardiovascular computational mechanics simulations
- Perform all other duties as assigned to support Baylor’s mission
- Ability to comply with university policies
- Maintain regular and punctual attendance
A complete application will contain the following documents:
- Up-to-date CV
- Contact information for two professional references
- Cover letter detailing research background and interest in the position
What You Can Expect
As part of the Baylor family, eligible employees receive a comprehensive benefits package that includes medical, dental, and vision insurance, generous time off, tuition remission, and outstanding automatic retirement contributions. Baylor has a comprehensive benefits plan that supports you and your family’s wellbeing and allows you to be a part of the life of a vibrant and active college campus. To learn more, go to Baylor Benefits & Advantages.
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