Master Thesis

Materials Center Leoben (MCL) supports numerous companies in the

production sector developing high-performance materials,

manufacturing processes and products. MCL designs specific computer
- aided technologies in order to accelerate innovation processes in

manufacturing companies as well as to support the digitalization of the

value chain and products. Our portfolio includes cooperative research

and development projects with international and national partners from

the production and research sectors as well as several consulting,

laboratory and simulation services in materials science.
- We are looking forward to knowing you_

**What are we looking for. Your challenge**

**Our offer**

**Machine-Learning potentials for Grain Boundaries**

A motivated master student from materials
science, physics, or computational sciences (e.g.,
computational mechanics, applied mathematics)
with one (or more ) of the following skills
- crystallography/atomic-scale mechanics,
- atomistic simulation,
- linear algebra/machine-Learning methods,
- programming (Python, C++).

In this work, you will study grain boundaries—
planar defects which influence and determine
many mechanical properties of alloys. More
precisely, your challenge is to
- develop an algorithm for training machine
- learning potentials on grain boundaries,
- develop a Monte-Carlo algorithm for

simulating grain boundary segregation as a
function of the composition of the alloy,
- develop a machine-learning model that

efficiently predicts segregation energies over
the whole alloys space.
- The precise tasks may depend on your
background and personal preferences.

**Master Thesis: Machine-Learning Algorithms for**
**Simulating Grain Boundary Segregation in Alloys**
- Reference No.: MCL_233_
- These topics inspire you or you are already familiar with them? _
- Then you are the right person for this position_

An employment with immediate start and a gross salary of € 3.000;
- for six months.