Job Advertisement: Post-doc position

Post-doc (m/f/d) in the field of atomistic simulations/machine learning or Machine Learning / Deep Learning.

OUR PROFILE 

As part of the Network for National High-Performance Computing (NHR), RWTH Aachen University and TU Darmstadt combine their strengths in High Performance Computing (HPC) applications, algorithms, and methods as well as in the efficient allocation and use of HPC hardware. As a joint NHR Center for Computational Engineering Sciences (NHR4CES), the two sites provide targeted

 

support for engineering applications, especially with regard to complex flow scenarios, energy conversion, materials design, and engineering-oriented physics, chemistry, and life sciences. In this context, the use of machine and deep learning approaches in Materials Design will play a central role.

You will work as post-doc in the NHR4CES Simulation and Data Lab “Materials Design” at RWTH Aachen University. Your focus will be either on subject a or subject b.

Subject a: The development of machine learning interatomic potentials for compositionally complex materials

In the past decade various families of machine learning interatomic potentials have been developed that show accuracies comparable with density functional theory (DFT) calculations. However, most of these potentials so far have been trained for elemental/single component systems. As the SDL Materials Design at RWTH Aachen we are interested in compositionally complex materials often referred to as high entropy alloys. Our goal is to train and develop interatomic potentials suited for the description of compositionally and structurally complex materials. 

 

YOUR PROFILE

You should have a background in atomistic simulations (DFT and/or classical MD) and be familiar with machine learning algorithms

The position will involve collaboration within several teams, so that good communication skills and enjoyment of collaborative team work are required.

YOUR TASKS

You will work as post-doc in the NHR4CES Simulation and Data Lab “Materials Design” at RWTH Aachen University. Your focus will be the development of machine learning interatomic potentials for compositionally complex materials.

Subject b: Computer vision for electron microscopy.

Images from scanning electron microscopy and Plasma focused ion beam (FIB) tomography can be further analyzed and quantified via Computer vision algorithms. As the SDL Materials Design at RWTH Aachen we plan to adapt different families of deep convolutional neural networks to learn microscopy images in supervised and unsupervised/self-supervised fashion. The focus of this work would be to design semantic segmentation/object detection networks most suited and optimized for our microscopy data.

YOUR PROFILE

You should have a background in machine learning/deep learning and be familiar with high performance computing.

The position will involve collaboration within several teams, so that good communication skills and enjoyment of collaborative team work are required.

YOUR TASKS

You will work as post-doc in the NHR4CES Simulation and Data Lab “Materials Design” at RWTH Aachen University. Your focus will be on Computer vision for electron microscopy.

Regardless of the chosen subject, as a member of the NHR4CES, a part of your task will be to provide scientific support to the community and participate in training sessions for our scientific community.

Hiring Date: As soon as possible

Temporary Employment: Temporary limited to December 31, 2025 with the option of extension

Weekly Working Time: Full time (a part-time employment is possible on request)

Post-Doc Opportunity: Exists

Assessment Of The Position: Salary according to TV-L, E13

Contact

Dr. Sherri Hadian

RWTH Aachen University

Michaela Bleuel

RWTH Aachen University