De:	neutron-bounces@neutronsources.org de la part de Weigandt, Kathleen M. (Fed) 
<kathleen.weigandt@nist.gov>
Envoyé:	mercredi 6 novembre 2019 16:02
À:	neutron@neutronsources.org
Objet:	[Neutron] Image Analysis and Machine Learning Postdoc Opportunity	at NIST
Pièces jointes:	ATT00001.txt

Title: Computational Metrology for Neutron Interferometric Microscopy
Job Position: Postdoctoral researcher at NIST
Keywords: imaging; image analyses; machine learning; software engineering

Project Description: NIST is developing a novel neutron interferometric phase imaging method 
using a grating-based, far-field interferometer with 1000× increase in time resolution and 10× 
improvement in spatial resolution over prototypes. Such an instrument enables to measure the 
structure of heterogeneous systems including ageing concrete, blood clots, highly-efficient 
batteries, multiphase flow in colloidal and geological systems, and the burgeoning field of metal 
3D printing. As this far-field neutron interferometer will be generating terabytes of image data 
per day, there are several open research problems that would be the research focus of a 
postdoctoral candidate. The problems include finding optimal imaging configurations, 
accelerated 3D tomographic reconstruction from multiple 2D images and multiple channels, 
optimization of 2D projection viewpoints (dose reduction and time savings), the use of artificial 
intelligence and traditional machine learning models for 3D volume segmentation, designing 
tools for 3D segmentation annotation and verification, registration of 3D volumes, evaluations of 
accuracy and uncertainties of image analyses, visualization of multiple terabyte-sized 3D 
volumes, quantification and statistical analyses of volume characteristics, and dissemination of 
petabyte-sized 3D image collections with web-based visual inspection and browsing capabilities. 
A small subset of project relevant publications is listed below. A candidate should have at least a 
master’s degree in computer science or related field and have background in imaging, image 
analyses, machine learning, and software engineering.


References:
(1) A. J. Brooks et al, “Neutron interferometry detection of early crack formation caused by bending fatigue in 
additively manufactured SS316 dogbones,” Materials and Design, 140 (2018) 420–430 
https://doi.org/10.1016/j.matdes.2017.12.001

(2) D. A. Pushin et al, “Far-field interference of a neutron white beam and the applications to noninvasive
phase-contrast imaging,” PHYSICAL REVIEW A 95, 043637 (2017)

(3) T. Pipatsrisawat et al, “Performance Analysis of The Filtered Backprojection Image Reconstruction Algorithms,” 
ICASSP 2015.

(4) D.M. Pelt and J.A. Sethian, “A mixed-scale dense convolutional neural network for image analysis,” PNAS, 
January 8, 2019.

If interested then, please, contact:
Peter Bajcsy, PhD
Information Technology Laboratory (ITL)
National Institute of Standards and Technologies (NIST)
Phone: 301-975-2958
Email: peter.bajcsy@nist.gov
URL: https://www.nist.gov/people/peter-bajcsy