Staff Scientist

About the role

The SurgBioMech Lab is run by Dr. Luka Pocivavsek and Dr. Nhung Nguyen in the Department of Surgery. It is an interdisciplinary lab that combines the physical sciences, engineering, and medicine. We apply principles from differential geometry and mechanics to understand aortic pathology, stability and failure of endovascular repairs, stress focusing phenomena induced by surgical interventions, and interfacial adhesion, surface renewal, and topography. We utilize advanced computational methods to extract and define geometric feature spaces that can be linked to mechanical instabilities from patient-derived data; finite element methods to model a variety of complex systems, geometries, and materials under different loading conditions; computational fluid dynamics to study stability of seal zones with flow; micro-computed tomography to detect the underlying fiber-reinforced structures of tissues; x-ray scattering techniques to probe the structure and interactions of cell monolayers on solid substrates; and Langmuir monolayers to study the wrinkling, folding, and collapse behaviors of films at air/water interfaces.

Responsibilities

• Supports machine learning and artificial intelligence in the SurBioMech lab.
• Supports ongoing research efforts of quantification of aortic geometry derived from CT scans.
• Closely works with collaborators and stakeholders in computer science on a conformal mapping project.
• Applies research principles and relevant subject matter knowledge relevant to administer a research project.
• With a moderate level of direction, manages lab and/or research-related duties and tasks.
• Helps develop, design and conduct research projects according to plan.
• Supports data collection and analytical needs of research projects.
• Conducts literature reviews and helps write reports and manuscripts.
• Ensures project compliance with different policies, procedures, directives, and mandates.

Qualifications

• Minimum requirements include a college or university degree in related field.
• Knowledge and skills developed through 2-5 years of work experience in a related job discipline.
• Education: Master’s Degree in Computer Science, Applied Mathematics, or a related quantitative field.
• Preferred qualifications include expert level proficiency in programming (Python, C++), advanced proficiency in neural network model development, training, and validation, advanced proficiency in computer vision, and proficient knowledge of differential geometry and topology.