Positions: Two Ph.D. Students and One Postdoctoral Researcher

Introduction:

The Computational Materials Physics group, led by Georg Kresse, at the University of Vienna, is pleased to announce exceptional research opportunities in collaboration with the "Cluster of Excellence: Materials for Energy Conversion and Storage." This prestigious cluster aims to address the global crisis caused by the combustion of carbohydrates through groundbreaking advancements in materials science. Our group plays a pivotal role in Pillar C of the cluster, where we focus on state-of-the-art developments and applications in machine-learned force fields, time-dependent density functional theory (TDDFT), and machine-learning of self-consistent charge densities. These cutting-edge techniques are already partly integrated into VASP (Vienna Ab Initio Simulation Package), the leading computational materials science software for condensed matter first-principles calculations, and we plan to extend these capabilities.

Research areas:

1. Machine-Learned Force Fields: Join us in the development of machine-learned force fields using advanced machine learning techniques. This research will enable the discovery of novel materials for energy conversion and storage applications, revolutionizing the field.
2. Time-Dependent Density Functional Theory (TDDFT): Contribute to the implementation of interatomic forces in excited states using TDDFT. By understanding electronic excitations in complex materials, we can design energy-efficient materials for photocatalysis and electrocatalysis.
3. Machine-Learning of Self-Consistent Charge Densities: Be part of our research efforts to develop machine learning algorithms that enable non-self-consistent density functional theory calculations. This innovation will open the way to million-atom calculations, which are crucial for simulating the electronic and transport properties of complex new materials using VASP.

Collaboration and Support:

The selected candidates will have the opportunity to work closely with a dedicated University Assistant, who will provide invaluable support throughout their research journey. Additionally, all implementation work will be conducted in close collaboration with the VASP team, as well as all experts within the "Cluster of Excellence: Materials for Energy Conversion and Storage."

Eligibility:

We welcome applications from enthusiastic individuals with a strong academic background in physics, chemistry, materials science, computer science, or related disciplines. Candidates should have a strong interest in pursuing innovative research at the intersection of machine learning and computational and experimental materials physics, with a focus on addressing the global energy crisis.

Ph.D. positions:

• Candidates applying for Ph.D. positions should hold a Master's degree (or equivalent) in a relevant field and demonstrate a passion for advancing knowledge in materials science through computational methods.

Postdoctoral position:

• Applicants for the Postdoctoral position should possess a Ph.D. in physics, materials science, or a related field. A proven track record of publications in computational materials science and expertise in machine learning will be highly advantageous.

Application Process:

Interested candidates are invited to submit their applications by September 18th, including a (i) detailed CV, (ii) lists of publications and talks including one or two reprints representative of previous research, (iii) a concise statement of research interests, and (iv) at least two letters of reference. Please send your application by email to Christine Schrempf .

Join the vibrant and dynamic Computational Materials Physics group at the University of Vienna and contribute to the groundbreaking research within the "Cluster of Excellence: Materials for Energy Conversion and Storage." Together, we can address the global crisis caused by carbohydrate combustion through innovative materials science.

For further information about our research group and the University of Vienna and the cluster of excellence, please visit https://cmp.univie.ac.at/  and http://coe-mecs.at/.

We eagerly await your applications and look forward to welcoming exceptional researchers to our team!

Georg Kresse

Computational Materials Physics Group, Faculty of Physics, University of Vienna