Postdoc in method development for physics based and data-driven simulation of excited state dynamics and light emission process in doped insulators -

The postdoc position involves developing new methodologies combining electronic structure theory and machine learning to predict the optical properties of doped insulators. The postdoc will focus on simulating electronic structure of defects with high throughput computations, and creating graph-representation learning models to accelerate those simulations towards understanding earth surface processes.
Are you looking for a 2-year postdoc position developing new methodologies for predicting optical properties of solids by combining electronic structure theory and applied machine learning to point defects (dopants) in wide bandgap systems?

The outcome of this project has an additionally far-reaching impact on the understanding and prediction of optical properties in doped insulators with applications in health technologies, quantum sensing, and clean energy technologies.

The research position is part of the ERC advanced grant project “ LUMIN: Illuminating Charge Transport in Feldspar to Measure Rates of Earth Surface Processes” at DTU Physics, where the new simulation methodologies developed during the postdoctoral project, in conjunction with novel experimental data, will enable us to determine how the Earth’s surface has evolved in the recent past.

Responsibilities and qualifications
Your key responsibilities in this postdoc position include developing new methodologies to predict optical properties of solids by integrating electronic structure theory with applied machine learning.

You will (a) test and integrate density functional theory and other electronic structure method-based simulations for studying defect ion’s energy structure with respect to the band edge, excited state life-time, electron-phonon coupling, and excitation/emission/absorption spectra (b) perform high throughput computation to study doped polyanionic systems with single or multiple types of defects and defect interactions (c) create graph-representation learning based models to simulate more realistic large systems (>1000 atoms) with multiple defects and defect interactions.

The project will be carried out in close collaboration with external partner groups that are considered world’s leading method development groups for materials simulation. The activities are linked to other ongoing projects in the Section working on clean energy materials and machine learning for accelerated materials discovery.

The qualifications required include a PhD in physics, materials science, chemistry, or a related discipline. You must have expertise in electronic structure theory, particularly density functional theory and related methods. Experience in applying machine learning techniques to scientific problems and proficiency in high throughput computational simulations are necessary. Demonstrated ability to work in collaborative research environments and a proven track record of high-quality research and publications in relevant areas are also required.

As a formal qualification, you must hold a PhD degree (or equivalent) in physics, chemistry or materials science. You have a strong background in electronic structure simulations, have good programming skills; are interested in learning machine learning methods. The successful candidate should be able to take responsibility for the progress and quality of project.

Assessment
The assessment of the applicants will be made by: Head of Section, Senior Researcher Mayank Jain, Head of Section, Professor Juan Maria Garcia Lastra and Associate Professor Arghya Bhowmik.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and terms of employment
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union.

The period of employment is 2 years starting from 1 November.

You can read more about career paths at DTU here.
Further information
Further information may be obtained from Prof. Juan Maria Garcia Lastra at [email protected] or Assoc. Prof. Arghya Bhowmik at [email protected]

Please do not send applications to these e-mail addresses, instead apply online as described below.

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.

Application procedure
Your complete online application must be submitted no later than 31 July 2024 (23:59 Danish time).

Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

• Application (cover letter)
  • Please reply to the following: do you have experience with electronic structure theory, simulation defect states and optical excitations?
• CV
• Academic Diplomas (MSc/PhD – in English)
• List of publications

Applications received after the deadline will not be considered.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply.

The Department of Energy Conversion and Storage (DTU Energy) focuses on research and development of functional materials, components, and systems for sustainable energy technologies. The technologies include fuel cells, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences on electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 270 dedicated employees. Read more about us at www.energy.dtu.dk.

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