Research Assistant/associate - Aachen, Deutschland - RWTH Aachen

RWTH Aachen

Geprüftes Unternehmen

Aachen, Deutschland

vor 3 Wochen

Geschrieben von:

Lena Wagner

beBee Recruiter

Beschreibung

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

Contact:

Name:

Simon Münstermann

Telephone:

- workPhone

***:

Contact:

Name:

Raheleh Hadian

***:

Institution:

Lehrstuhl für Werkstoffchemie

Our Profile:

Your Profile:

Subject a:
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.

Subject b:
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 Duties and Responsibilities:

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.

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.

What We Offer:

The position is to be filled at the earliest possible date and offered for a fixed term up to December 31st, 2025.

The fixed-term employment is possible as it constitutes one of the fixed-term options of the Wissenschaftszeitvertragsgesetz (German Act on Fixed-term Scientific Contracts).

This is a full-time position with the possibility of a part-time contract upon request.
The salary is based on the German public service salary scale (TV-L).
The position corresponds to a pay grade of EG 13 TV-L.

About us:

RWTH is a certified family-friendly University.

We support our employees in maintaining a good work-life balance with a wide range of health, advising, and prevention services, for example university sports.

Employees who are covered by collective bargaining agreements and civil servants have access to an extensive range of further training courses and the opportunity to purchase a job ticket.