Two postdoc positions in atomic-scale computational design of battery materials and interfaces

 The section for Atomic Scale Materials Modelling at DTU Energy, Technical University of Denmark (DTU), is looking for outstanding candidates for two, 2-year postdoc positions within the field of method development for accelerated design and discovery of next-generation sustainable battery materials and interfaces. The research projects are part of the European project BIG-MAP (Battery Interface Genome – Materials Acceleration Platform) under the large-scale, long-term European research initiative Battery 2030+ which seeks to reinvent the way we invent batteries. The BIG-MAP consortium is coordinated by DTU and brings together researchers across the battery discovery value chain from atoms to battery cells, totalling 34 partners from 15 countries and spanning world-leading academic experts, research laboratories and industry leaders.

Project description

The successful candidates will use a combination of electronic structure modelling, machine/deep learning algorithms and automated workflows to bridge different simulational codes and time/length scales in simulations and experiments on battery materials and interfaces. These positions are the first in the BIG-MAP project and will form the basis for the further work on the development of a closed-loop infrastructure for autonomous discovery of battery materials and interfaces. The aim of the projects is to:

1. Develop machine learning algorithms, which can learn to map multi-scale battery interface dynamics into hierarchically coupled latent spaces that each encode for structures at different length scales. Furthermore, such a model will be integrated with a suitable coupled Markov chain model in the extended latent space to yield a complete dynamics simulator, which can be validated against simulations of battery interface system dynamics at different length and time scales. Uncertainty propagation methods will be integrated within the probabilistic latent space generative models to provide predictive uncertainties that take into account both data and model uncertainties.
2. Demonstrate a working use case of experiments that drive simulations and simulations that drive experiments, thereby automating and accelerating the discovery process. This will involve several key steps to be performed in collaboration with the BIG-MAP partners, namely: 1) Gathering use-cases where linking of simulation and experiment is viable and identifying the current technological impediments. 2) Helping to establish, implement and put into use an ontology standard that enables interoperability at different scales. 3) Development of automated workflows in AiiDA, SimStack and/or ASE incorporating simulations, experiments and AI-driven methods/tools. 4) Delivering a working demonstrator of a feedback loop between experiments and simulation.

Qualifications

Candidates should hold a PhD or equivalent degree in computer science, physics, chemistry or materials science. The candidate must have a strong background in computational chemistry, physics or materials science and/or machine learning, and are expected to have performed original scientific research within the relevant fields listed above for the specific position(s). Moreover, the successful candidate:

• is innovative and able to work both independently and in cross-disciplinary teams
• has good communication skills in English, both written and spoken
• is able to work independently and take responsibility for progress and quality of projects.

Application

We must have your online application by 6 August 2020 (23:59 local time).

To view the full announcement and to apply: http://www.career.dtu.dk