About the Project
The candidate will work in an interdisciplinary team on the development of AI/ML methods and their application to materials science problems. We are in particular interested in graph convolutional neural networks or generative methods such as GANs, autoencoders or genetic algorithms. Application areas include organic chemistry and homogeneous catalysis, as well as organic semiconductors and hybrid organic-inorganic materials such as perovskites. Basic experience in python programming is required. ML libraries such as tensorflow, keras and/or pytorch are welcome. Alternatively, experience with quantum chemistry and materials simulation tools (DFT/MD) are useful.
During the project, the candidate will learn many useful and relevant tools (both for academia and industry) at the interface between computer science and materials science. We are currently building a young and interdisciplinary team of computational scientists. To make a real world impact, we aim at close collaboration with experimental groups and industry.
KIT is aiming at increasing the number of women in STEM and therefore particularly welcomes applications from women.
NeurIPS workshop 2019: https://arxiv.org/abs/1905.13741
ChemRxiv 2019: https://doi.org/10.26434/chemrxiv.10347566.v1
Full list of publications: https://scholar.google.ca/citations?user=3B5h6u0AAAAJ&hl=en
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