| he Structural Materials Division of the University of Dayton Research Institute (UDRI) is seeking a researcher with experience in applied and computational mathematics to work on Air Force funded research programs. The successful candidate will work with engineers and scientists to develop and apply quantitative and analytical methods to material systems relevant to nondestructive evaluation (NDE). A major goal of the current project is to utilize Matching Component Analysis (MCA) as a method of segmenting and registering the data from sensors of various modality. Prior work has shown that MCA can be used to enhance classification performance over traditional machine learning algorithms. We will explore this algorithm as a way of combining data to segment boundaries between regions of varying material property within the sample data set to develop a new machine learning tool for data segmentation and registration. The output of the algorithm will be used as an informative structural prior for Bayesian estimation of the relevant material property within the sensor field of view. The researcher would be working on advancements of the MCA algorithm and development of a Bayesian inversion routine for NDE characterization. Minimum Qualifications: P1 Minimum Qualifications Bachelors degree in applied mathematics, engineering, computer science, physics, or related field. Excellent written communication skills. Interest and ability to communicate and interact with multidisciplinary scientists. Potential to publish research results. MATLAB proficiency. Due to the nature of this position, incumbent must be a U.S. citizen and have the ability to pass a national agency check. P2 Minimum Qualifications Three years of directly related experience. Bachelor’s degree in applied mathematics, engineering, computer science, physics, or related field. Excellent written communication skills. Interest and ability to communicate and interact with multidisciplinary scientists. Potential to publish research results. MATLAB proficiency. Due to the nature of this position, incumbent must be a U.S. citizen and have the ability to pass a national agency check. Preferred Qualifications: Masters or PhD degree in applied mathematics, engineering, computer science, physics, or related field. Demonstrated experience with data-driven mathematical models, image analysis, physics based deep learning methods, nonlinear optimization methods and application of neural networks for modeling sparse data sets. Please summarize your ability to meet minimum qualifications in your cover letter. References may be requested later in the interview process. Informed by its Catholic and Marianist mission, the University is committed to the principles of diversity, equity, and inclusion. Informed by this commitment, we seek to increase diversity, achieve equitable outcomes, and model inclusion across our campus community. As an Affirmative Action and Equal Opportunity Employer, we will not discriminate against minorities, women, protected veterans, individuals with disabilities, or on the basis of race, color, national origin, religion, sex, sexual orientation or gender identity. |