Are you interested in combining the fields of Machine Learning and Computational Chemical Physics and Materials Science? In the group of Prof. Bjørk Hammer at Aarhus University, we are seeking new Post doctoral candidates to work with image recognition, supervised learning and reinforcement learning to facilitate structural search at the atomic scale.
About the position:
- The successful candidate(s) will identify and develop machine learning techniques that may speed up first principles calculations of equilibrium structures and reaction pathways in chemical physics.
- In recent years, the group has developed a number of techniques for global optimization
- One method, GOFEE, does its structural search in a model potential using Gaussian Process Regression and is guided by Bayesian statistics to perform occasional sanity checks with full density functional theory (DFT) calculations.
- Another method, ASLA, does self-training of image recognition for neural network agents that interact with a DFT program.
- The post docs will be introduced to these methods and be expected to develop their own improvements and to apply these in the search for the reactive state of matter ranging from interstellar dust clouds to industrial heterogeneous catalysts.
- Applicants must hold a PhD degree in physics, chemistry, nano-science, or computer science (or equivalent).
- Previous experience with machine learning methods and/or first principles energy calculations in physical chemistry is required.
- Experience with programming in python is highly desired.
How to Apply:
- The application must be submitted via Aarhus University’s recruitment system, which can be accessed under the job advertisement on Aarhus University’s website.
|Type of position||Postdoctoral|
|Subject areas||Physics, chemistry, Nano-science, or computer science|