You will conduct research within the QuATOMs group, collaborating with national and international experimental partners, write and publish scientific articles, develop software and scripts, actively participate in international and national congresses and workshops, and develop your soft- and technical skills via international research stays and (doctoral school) training courses, with the aim of writing a doctoral dissertation. You will get the opportunity to build expertise in density functional theory modelling, machine learning, and deep neural networks.
Your research concerns the topic: Augmenting DFT modelling of vibrational spectra through machine learning and deep neural networks using small data sets.
As a PhD student you have a strong background in one of the following fields: materials physics/chemistry, computational modelling, quantum chemistry/physics, spectroscopy. A combination of skills related to several of these fields will be considered as an advantage, though is not strictly required.
You are part of a large, interdisciplinary institute with a broad international and national network. You participate in the Doctoral School of Sciences & Technology. You will also have some limited teaching tasks in the Bachelor Chemistry and the Master Materiomics.
The UHasselt-IMO expertise group QuATOMs focusses on the quantum mechanical modelling and characterisation of materials (solids, surfaces & molecules). In the context of the design of new materials, this is done in collaboration with experimental partners, corroborating and elucidating the experimental findings. However, as the complexity of the experimental system grows, so does the computational cost of the quantum chemical simulations. Therefore, the QuATOMs group is seeking to augment and supplement the capabilities of standard quantum mechanical calculations with artificial intelligence (AI) methods. The latter ones are trained on small datasets, in contrast to the standard big data approach, thus improving the applicability for new materials design problems.
Within this project the focus lies on the modelling of vibrational spectra (phonons, IR, & Raman), specifically to finger print defects in diamond, and characterize large scale weakly bonded crystals. The aim is to extend beyond what is possible with standard quantum mechanical approaches using both machine learning and deep neural networks, but at a minimal training cost.
The QuATOMs group is therefore looking for applicants (m/f/x) with a strong interest in both quantum and AI modelling of materials, an affinity for computational research, motivated to unravel the fundamentals of atomic vibrations and mater-light interactions, in close collaboration with experimental partners working on diamond and hybrid materials.
You will be appointed and paid as PhD student.
You will be appointed as a PhD student for 2x2 years after a positive evaluation.
The selection procedure consists of a preselection based on application file and an interview.
Apply up to 15.02.2023