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Artificial Intelligence and Machine Learning for Natural Sciences


Project Description

The recently founded AIMat group at the Karlsruhe Institute of Technology (KIT) is searching for motivated students with a above-average Masters degree in computer science or (computational) natural sciences. Ideally, the candidate has a strong background in machine learning and artificial intelligence or knowledge/experience in computational materials sciences (quantum chemistry or similar).

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.

Funding Notes

The candidate will be paid according to TV-L E13 (100%). The position is funded by the Artificial Intelligence program of Baden Württemberg („Künstliche Intelligenz Baden-Württemberg“ (KI-BW).)

References

ICLR 2020: https://openreview.net/forum?id=H1lmyRNFvr
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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