Job Postings

Please send an e-mail to dmi@iucr.org if you have a DMI related posting you would like advertised here.

 

Postdoc - X-Ray Microscopy and Materials Physics at DTU/ESRF

The new hard X-ray microscope at ID03 at the European Synchrotron Radiation Source (ESRF) will start operation end of 2023. In this project we aim to visualize the self-organization taking place when metals deform plastically. We will directly see defects (dislocations) interacting and forming boundaries and we see how the grain-assembly builds up with external load.  Our aim is to use this advantage to generate a new generation of materials models, that are based on first principles. Coupling the vast 3D experimental movies with the most advanced dislocation dynamics simulations requires novel data science approaches, e.g. based on AI.

Join our ERC center: “The physics of metal plasticity” to help fulfill this vision. Based at the Technical University in Denmark you will be working closely with the experimentalists building the instrument and at the same time be in direct contact with leading groups in simulations, e.g. Prof. El-Azab at Purdue University. You will also be part of a Danish Center-of-Excellence on hard materials in 3D, SOLID, where you will interact with 15 other PhDs and post docs, all exploiting the latest 3D methods based on large scale x-ray and neutron facilities, within a broad range of fields.

For more information and to apply, please see here or contact hfpo@dtu.dk.

 

Postdoc - X-ray Microdiffraction Imaging

The Surface Scattering and Microdiffraction (SSM) group in X-ray Science Division (SSM-XSD) of Advanced Photon Source (APS) at Argonne National Laboratory leads pioneering efforts in developing cutting edge X-ray instrumentation and techniques, for enabling in-situ experiments with x-ray diffraction microscopy investigation in areas of physics, chemistry, and material sciences. 

The coming APS upgrade (APS-U) will enable X-ray diffraction microscopy to image in-situ structural information with a 150 nm focused beam. To accomplish this, we are currently seeking to fill a Postdoctoral Appointee position for a 2+(1)-year period to support development of data analysis and computational algorithms for X-ray diffraction microscopy experiments at instrument 34-ID-E and electron microscopy studies at Argonne’s Center for Nanoscale Materials. 

The successful candidate will participate in novel in-situ experiments with precise control of sample nano-indentation and/or corrosion. The candidate will work in a multidisciplinary team, including physicists, chemists, and engineers, and will lead the development of data analysis pipelines in support of innovative in-situ experiments.  These developments will include software/algorithms (including machine learning) for multimodal data analysis, with a goal of providing real-time feedback of effects seen in the sample during the on-going experiment.

For more information and to apply, please see here.

 

Postdoc - AI/ML for High-Energy X-Ray Diffraction Microscopy

The X-ray Science Division (XSD) at Argonne National Laboratory invites applications for postdoctoral researchers position for a project to develop artificial intelligence (AI) and machine learning (ML) methods to enhance high-energy X-ray diffraction microscopy at the Advanced Photon Source. The extreme volume and velocity of information associated with this non-destructive microstructure mapping technique can benefit from AI/ML at each stage of data flow, from the sensor to the data center. Because such tools can run at high speeds, thanks to advances in AI streaming inference accelerators, it becomes feasible to extract salient information from in-flight data, in real time, and thus both enabling fast feedback and reducing downstream computational burden. The successful candidate will conduct cutting-edge research in data science and deep learning and apply it to scientific problems, particularly in the materials science and engineering fields. The candidate will play a key role in developing physics-aware AI/ML models, developing workflow building blocks and implement high-speed training on data center AI systems (e.g., Cerebras CS-1 ML accelerator and Argonne's Aurora exascale supercomputer), end-to-end model training workflows and explore AI accelerators for simulation applications.

For more information and to apply, please see here.

 

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