The University of Luxembourg aspires to be one of Europe’s most highly regarded universities with a distinctly international and interdisciplinary character .
It fosters the cross-fertilisation of research and teaching , is relevant to its country, is known worldwide for its research and teaching in targeted areas, and is establishing itself as an innovative model for contemporary European Higher Education.
The Faculty of Science, Technology and Medicine (FSTM) contributes multidisciplinary expertise in the fields of Mathematics , Physics , Engineering , Computer Science , Life Sciences and Medicine .
Through its dual mission of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens, in order to better understand, explain and advance society and environment we live in.
The University of Luxembourg invites applications to the following vacancy in the Department of Physics and Materials Science () within its Faculty of Science, Technology and Medicine :
Postdoctoral researcher in quantum machine learning for chemical discovery
The University of Luxembourg is looking for a postdoctoral researcher to start working on the ERC Proof-of-concept (PoC) project DISCOVERER : A novel chemical discovery platform enabled by machine learning .
Computational design and discovery of molecules and materials relies on the exploration of increasingly growing chemical spaces.
The discovery and formulation of new drugs, antivirals, antibiotics, catalysts, battery materials, and in general chemicals with tailored properties, require a fundamental paradigm shift to search in unchartered swaths of the vast chemical space.
This is in stark contrast to current approaches, which start from (commercially available) libraries of compounds from various suppliers.
During the last years, it has been a substantial advance in modeling and understanding the behavior of molecules in complex environments.
As a result, diverse machine learning and physics- based methods were developed to deal with covalent and non-covalent interactions that allow an accurate and efficient modelling of molecules of increasing size (from 10 to 1000 atoms).
These methods now enable routine calculations of quantum-mechanical properties of molecules throughout chemical compound space, provided that enough reference data is produced as a starting point for training.
Within DISCOVERER, we aim to promote a paradigm shift in chemical discovery by inverting the selection pyramid by starting with pre-defined parameters from which new chemical entities are designed through machine learning and AI-enabled algorithms.
We can do so by integrating these modules into a commercial platform : Chemical Space Machine . DISCOVERER’s main goal is to finalize the development of a commercial alpha version of Chemical Space Machine and setting up its commercialization strategy.
The candidate will be integrated into the activities of the Theoretical Chemical Physics group lead by Prof. Alexandre Tkatchenko (www.
tcpunilu.com). This project will allow the candidate to interact with the scientific staff from some of the world’s top universities and to develop the building blocks of a startup in ML-based chemical discovery.
The candidate’s tasks include :
We are looking for enthusiastic candidates with a Doctor degree in Physics, Chemistry, Material Science or Computer Science.
Experience in the field of quantum chemistry, with a special focus on data-based and data-intensive approaches and chemical discovery is considered advantageous.
Personal initiative, the ability to work independently as well as team-oriented research and language skills (English) are expected.
We are looking for a proactive young scientist who also has inspirations in the commercial sector. Applications from women are particularly welcome.
The same applies to people with disabilities.