PhD position (m/f/d) in computational chemistry and machine learningHeidelberg Institute for Theoretical Studies and Heidelberg University • Computational Carbon Chemistry • Heidelberg

Time Span as soon as possible for 4 years Application Deadline 31 Mar 2023 Financing yes Type of Position

Field of Research

Subjects chemistry, physics, materials science, or a related discipline Description The Computational Carbon Chemistry (CCC) group at the Heidelberg Institute for Theoretical Studies (HITS gGmbH) is looking to fill a

PhD position (m/f/d) in computational chemistry and machine learning

to work on the ERC Starting Grant “PATTERNCHEM: Shape and Topology as Descriptors of Chemical and Physical Properties in Functional Organic Materials”.

Your role

Your interdisciplinary research project will involve developing and implementing novel machine learning representations and fingerprints of shapes and topologies for diverse functional organic materials, including graphene derivatives, covalent organic frameworks, and hyperbranched polymers. You will also use high-level quantum-chemical simulations to design custom descriptors of the non-covalent interactions between materials and small molecules. The ultimate goal of PATTERNCHEM is to build an all-encompassing, adaptable framework for modelling interactions of multifaceted functional organic materials with their molecular targets, filling the missing links with newly devised structural fingerprints and energetic descriptors.

What qualifies you for this job

​Successful candidates should have a Master's degree (or be close to completion) in chemistry, physics, materials science, or a related discipline, and, preferably, an experience in theoretical chemistry, numerical simulations, data science, and coding.

See full announcement at: https: // www. h-its.org/de/hits-job/phd-position-m-f-d-in-computational- chemistry-and-machine-learning/

Working Language

Language of Dissertation

Required Documents

More Information https: // www. h-its.org/de/hits-job/phd-position-m-f-d- in-computational-chemistry-and-machine-learning/