A post-doctoral position is open in the group of Dr. Pavlo Dral in College of Chemistry and Chemical Engineering at Xiamen University for the development of cutting-edge machine learning and quantum chemistry methods. The earliest starting date is January 2020. Contract duration is 2 years.

The ideal candidate should be independent, creative, highly motivated, and willing to take up an ambitious challenge. The candidate must have experience with quantum chemistry method and software development. Experience with machine learning is preferable. PhD degree in chemistry or related field is required. Good skills in Fortran and Python, and a good command of English are expected. All candidates will be evaluated based exclusively on merit; no preferences will be given to any gender, nationality, family situation etc.

Xiamen University is an excellent place for conducting research and living. It is family-friendly with a kindergarten on the campus and a primary school near the campus. Starting salary is 200,000 RMB per year and may be increased based on performance. The University does its best to offer for renting a low-cost accommodation on the campus or nearby.

Applications should be sent directly to Dr. Pavlo Dral (dral@xmu.edu.cn). More information about the group’s research can be found on http://dr-dral.com.

Posted on: 22.10.2019.

Download printable version: Post-doctoral_opening_XMU.pdf

Tagged with: , , ,

I am happy to announce that I am joining Xiamen University as an Associate Professor.

(more…)

Prof. Walter Thiel passed away unexpectedly on August 23, 2019.

(more…)
Tagged with:

The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations with Machine Learning are reported.

(more…)
Tagged with: , ,

Johannes Margraf and I have published our perspective on what semiempirical molecular orbital (SEMO) methods are and should be approximating in the article[1] dedicated to the 70th birthday of our PhD supervisor Tim Clark.

(more…)
Tagged with: , , , ,

MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.

(more…)
Tagged with: ,

We have introduced two new NDDO-based semiempirical quantum-chemical methods ODM2 and ODM3, which are more consistent and accurate than other existing methods of this type.

(more…)

Tagged with: , , , , , , ,

We comprehensively analyzed the validity of the NDDO (neglect of diatomic differential overlap) approximation, which forms the basis for most modern semiempirical quantum chemical methods.
(more…)

Tagged with: , , , ,

We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics of molecular systems.
(more…)

Tagged with: , , ,

Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.
(more…)

Tagged with: , , ,