Postdoctoral Appointee - Machine Learning for Computational X-Ray Science

Key Skills

Effective communication skills, Image processing, Machine learning techniques, Python Programming, PyTorch, Statistics, TensorFlow

Job Description

The Advanced Photon Source (APS) is one of the most productive X-ray light source facilities in the world and conducts a wide range of cutting-edge research from battery materials to COVID-19 vaccines. The upcoming APS upgrade will further increase its source brightness and enable more applications with higher resolution and imaging rate. To achieve this goal, the X-Ray Science Division is seeking a postdoctoral candidate to develop robust and effective algorithms to automate data analysis and experimental control in computational microscopy including tomography, ptychography, and fluorescence imaging. The work involves utilizing state-of-the-art machine learning techniques to optimize parameters for experimental data acquired at the APS. The candidate will be part of a multidisciplinary team of beamline scientists, mathematicians, and software engineers.

Position Requirements

Education and Experience Requirements

• Recent or soon-to-be completed PhD (typically completed within the last 0-3 years) in statistics, applied mathematics, physics, or related scientific or engineering field with a strong emphasis on machine learning.
• Expertise in Python or other scientific programming languages
• Demonstrated strong analytical and problem-solving skills
• Effective verbal and written communication skills
• Mixture of experience and interest in learning the following topics: image processing, black-box optimization, recurrent neural network, dimension reduction and statistical analysis of high-dimensional datasets

Desired but not required knowledge

• Computational microscopy such as tomography and coherent diffraction imaging
• Experience one of the following: Keras, Pytorch, Tensorflow
• Experience in software development