University of Dayton Research Institute, Structural Materials Division

Position ID: 2617-POSTDOC [#17060]
Position Title: Applied Mathematician Post Doc
Position Type: Postdoctoral
Position Location: Dayton, Ohio 45469, United States [map]
Subject Area: Applied mathematics
Application Deadline: (posted 2020/12/17, listed until 2021/02/27)
Position Description:    

The Structural Materials Division of the University of Dayton Research Institute (UDRI) is seeking a postdoctoral researcher with experience in applied and computational mathematics to work on an on site contract at the Air Force Research Laboratory (AFRL) Materials State Awareness branch. The successful candidate will work with UDRI and AFRL scientists to develop and apply quantitative and analytical methods to material systems relevant to nondestructive evaluation in the U. S. Air Force. 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 for transfer learning applications to enhance classification performance over traditional machine learning algorithms by combining real and simulated data using only salient features from both. 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, resulting in 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 component orientation distribution function (cODF) of each micro-texture region (MTR) within the sensor field of view. The post-doc researcher would be working on advancements of the MCA algorithm and development of a Bayesian inversion routine for MTR orientation characterization. This position is available immediately and will remain open until filled.

Minimum Qualifications: PhD 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: 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.

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.

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