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PhD studentships in the use of computation and experiment for accelerated discovery of organic materials


   Department of Chemistry

This project is no longer listed on FindAPhD.com and may not be available.

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  Dr Kim Jelfs, Dr R Greenaway  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

PhD positions in the groups of Dr. Kim Jelfs (jelfs-group.org) and Dr. Becky Greenaway (greenawaylab.com) at the Department of Chemistry, Imperial College London, are available focused upon developing approaches for the accelerated discovery of organic materials or catalysts. The positions are available for October 2022 start.

Depending on the candidate, these projects can be computationally focused, including in the use of artificial intelligence as well as direct computational chemistry simulations in the group of Dr. Kim Jelfs, or focused upon synthesis and automation in the group of Dr. Becky Greenaway, or a combination of both.

This includes several studentship options with the EPSRC Centre for Doctoral Training in Next Generation Synthesis & Reaction Technology (CDT React) in the Department of Chemistry. More information on the additional training provided for those studentships can be found here: imperial.ac.uk/next-generation-synthesis-reaction-technology/the-cdt-programme/


Funding Notes

The positions are available to candidates holding, or about to hold, a Masters degree in Chemistry, or in a related field if the applicability of the experience can be demonstrated. Full funding is available for Home students. Interested applicants are encouraged to contact Dr. Kim Jelfs ([Email Address Removed]) and/or Dr. Becky Greenaway ([Email Address Removed]) by email, describing their interest in the field along with a CV.

References

More information about the groups’ recent work can be found in: “Artificial Intelligence Applied to the Prediction of Organic Materials” in “Machine Learning in Chemistry: The Impact of Artificial Intelligence”, DOI: 10.1039/9781839160233; J. Chem. Inf. Model. (2021), 61, 9, 4342; J. Chem. Phys. (2021) 154, 214102; Adv. Mater. (2021), 33, 11, 2004831; Chem. Sci. (2021), 12, 830; ChemPlusChem (2020), 85 (8) 1813; Nanoscale (2020), 12, 6744; Angew. Chem. Int. Ed. (2019), 131 (45) 16421; Chem. Mater. (2019) 31, 3, 714; Nature Commun., 2018, 9, 2849; Chem. Sci., 2019, 10, 9454.

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