research associate (m,f,x), for 4 years, 39.83 hours per week, TVL E13

The Chair of Atomistic Modelling and Simulation at ICAMS is looking for a

research associate (m,f,x), for 4 years, 39.83 hours per week, TVL E13

The Interdisciplinary Centre for Advanced Materials Simulation (ICAMS) focuses on the development and application of a new generation of simulation tools for multi-scale materials modelling with the aim of reducing development cost and time for new materials. The different length scales from the atomic structure to macroscopic properties of materials are bridged by an interdisciplinary team of scientists from engineering, materials science, chemistry, physics and mathematics.
Machine learning interatomic potentials enable the representation of atomic interactions with quantum mechanical accuracy. The efficiency of machine learning interatomic potentials allows for simulations with thousands of atoms and millions of time steps, which can be exploited for the computation of free energies. With the advertised position we are targeting the computation of complete multicomponent phase diagrams from machine learning interatomic potentials, including all relevant contributions to the free energy of the competing phases.
extent: full-time
duration: temporary beginning: as soon as possible
application deadline: 10.02.2025

Your tasks:

• Theory of phase transformation and phase diagram calculation from atomic interactions
• Development of efficient strategies and algorithms for the computation of phase diagrams
• Implementation and application of software for semi-automated computation of phase diagrams from interatomic potentials

Your profile

We expect an excellent or very good doctoral degree (PhD) in physics, chemistry, materials science or related disciplines and a strong expertise in code development, atomistic simulation, machine learning and statistical mechanics, in particular:

• Background in atomistic theory
• Proven track record in thermodynamic simulation of materials
• Strong expertise in code development
• Experience with machine learning