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Training in: Electronic structure simulation techniques including continuum envelope function and density functional theory techniques, deep learning, and evolutionary strategies.
Materials with functionalities of relevance to technology were often discovered by a combination of intuition-driven trial and error and lucky accidents. Given the vast number of unexplored alternatives, this approach has crucial limitations. The best system ever devised for making choices from an almost infinite set of alternatives is evolution itself. This led us look for a systematic approach, namely the inverse design method with artificial evolutionary strategies at its core.
Our approach is to employ evolutionary strategies to systematically scan the configuration space for optimal design candidates. The configuration space is spanned by the morphological parameters describing the nanostructure in question. The associated target properties could be a given wavelength, maximal oscillator strength or wave function overlap, respectively, at the desired wavelength, or – if memory cells are looked for – hole localization energies respective retention times.
For this purpose, we employ different methods for solving the Schrödinger equation as backend using advances software packages such as Nextnano and TiberCad. The candidate will learn how to operate these programs and to analyze the electronic structure of various types nanomaterials. The prospective inverse design module will be established on top of the related QM software.
How to Apply
- An internal application form listing your academic and job records (.docx template available here).
- A free format CV (pdf format max 2 Mb)
- Official documentation such as degree and grades certificates will be required at a later stage.