Two postdoctoral positions are available in computational discovery of molecular materials, working with Prof. Graeme Day at the University of Southampton (http://www.crystalstructureprediction.net). Applications are invited from candidates with skills to work on the integration of chemical space exploration with crystal structure and property prediction.

Both posts are available initially for 3 years and are funded by a European Research Council Synergy Grant. The project, ADAM – Autonomous Discovery of Advanced Materials – brings together state-of-the-art predictive computational chemistry methods, robotics, machine learning and materials characterisation through a collaboration between research groups at the universities of Southampton, Liverpool and Rostock. 

Candidates should apply through the https://jobs.soton.ac.uk website, job reference 1359721EB-R: https://jobs.soton.ac.uk/Vacancy.aspx?ref=1359721EB-R .

The closing date is 2 December, 2021.

Idea candidates will:

• have computational chemistry experience that can be applied to the discovery of new molecules with promising properties;
• have experience with large-scale calculations ad the use of high performance computing facilities;
• programming experience, ideally in Python;
• a collaborative mind-set and desire to work in an inter-disciplinary team.

Informal enquiries can be sent to Graeme Day at [email protected].

Please see the following for examples of recent work from our group that is related to the project:

Digital navigation of energy-structure-function maps for hydrogen-bonded porous molecular crystals, Nature Communications, (2021), 12, article number 817. https://doi.org/10.1038/s41467-021-21091-w

Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery, Chemical Science, 11, 4922-4933 (2020), https://doi.org/10.1039/D0SC00554A

Photocatalytic Proton Reduction by a Computationally Identified, Molecular Hydrogen-Bonded Framework, Journal of Materials Chemistry A, 8, 7158-7170 (2020), https://doi.org/10.1039/D0TA00219D

Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights, Chemical Science, 10, 9988-9997 (2019), https://doi.org/10.1039/C9SC02832C

Functional materials discovery using energy-structure-function maps, Nature, 543, 657-664 (2017), https://doi.org/10.1038/nature21419