PostDoc Position in Machine Learning for Material Science

PostDoc Position in Machine Learning for Material Science

TUM - Technical University Munich

Munich, Germany

WHAT:

Development of a new simulation platform for high-throughput screening and in-silico synthesis of new organic semiconductors (small molecules and polymers) with top electrical and optical properties for field effect transistors and solar cells. Towards a fast rational design of organic semiconductors.

HOW:

  • Several in-silico strategies to switch to a rational design for OS have been proposed;
  • They are based on multiscale approaches where the different spatial scales of electrical and optical properties are addressed in a hierarchical multiscale numerical model;
  • However, such methods are tremendously computational expensive;
  • The project goal is a Multiscale-Multiphyisics-Machine (Deep) Learning (ML/DL) approach where Multiscale models are merged with state-of-the-art Machine (Deep) Learning algorithms to speed up the calculation;
  • This reduces significantly the computational cost (up to 100 times faster).

The project is a purely theoretical work at Prof. Alessio Gagliardi´s group (Technical University Munich).

SPECIFIC TASKS:

  • Develop new machine (deep) learning approaches to speed up multiscale simulations for OS electrical and optical properties;
  • Use of different numerical models: atomistic density functional theory, molecular dynamics, kinetic Monte Carlo;
  • Excellent programming skills (C++, Object Oriented Programming);
  • Supervising PhD students and Master students;
  • Teaching skills

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© EuroJobsites 2019

EuroJobsites is a UK registered company number: 4694396 VAT number: GB 880 9055 04

Registered address: EuroJobsites Ltd, Unit 8, Kingsmill Business Park, Kingston Upon Thames, London, KT1 3GZ, United Kingdom

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