1) Smart recycling of battery materials (PhD). We will apply complex AI optimization techniques to battery materials recycling simulations, to make our current processing more sustainable by reducing waste and overuse of resources.
2) Natural langage processing for materials discovery (PhD). We will compile materials science literature into text-based datasets and train sophisticated neural networks to generate new knowledge on material-material and material-property relationships.
3) Advanced Bayesian methods for materials optimisation (postdoc PD). We plan to combine different channels of information to solve multi-objective Bayesian optimization problems in materials science, accelerate scientific discovery and materials design.
We welcome candidates with a background in physics, chemistry, materials science or computer science who are curious about applied machine learning in natural sciences. Prior experience with coding, materials simulations and machine learning experience is a bonus. We seek colleagues who enjoy programming, scripting and analytics, and are keen to push the boundaries of computational materials design. This project requires creative thinking, technical skills and a broad understanding of electronic phenomena in organic and inorganic materials.
The application deadline is 24.01.2022. We would like the successful candidate to start as soon as possible.
More information: On the positions and application guidelines are available here.