Location: Shanghai, China                                                                  

Duration: 1 year (possible extension by 1 year)

Starting date:  Immediate                                           

The rational design of functional polymers with targeted application properties is of strong interest to both industry and academia. Within this context, synthetic aspects play a key role: depending on the chemical structure of the monomers and reaction conditions, different polymer compositions and molecular weights can be obtained. Unfortunately, the underlying relationships are highly complex and they need to be established on a case-by-case basis. Solvay aims at speeding up the design of functional polymers by using a combination of computational tools, high-throughput experimentation, and machine learning techniques.

Vacancy description

We are looking for a computational chemist to improve the understanding of the reactivity of monomers of interest to Solvay, and ultimately build a database of computational monomer reactivity data. This data will be combined with experiments obtained at Solvay and academic partners, and explored by machine learning techniques. The objective is to develop robust predictive models for the synthesis of functional polymers.

The researcher will be part of a team dedicated to the modeling of chemical reactivity at Solvay’s Research & Innovation center in Shanghai. He / she will work in close collaboration with Solvay scientists and academic partners. This position is of particular interest to researchers who would like to apply quantum chemistry to problems of industrial interest, and become familiar with machine learning techniques.

Profile

• Doctorate or post-doctorate in computational polymer or organic chemistry
• Excellent track record in applying or developing quantum-chemical (DFT or wavefunction-based) methods for the modeling of polymerization kinetics or reactivity in organic chemistry
• Experience with one or more additional techniques, such as kinetic modeling, conformational search techniques, simulation of solvent effects, …
• Experience with machine learning / QSPR is not required but welcome
• Ability to work independently, proactively, and problem-oriented
• Proficient use of quantum-chemical software (Turbomole, Gaussian, Q-Chem, …)
• Good English communication skills

Interest candidates are kindly asked to submit a letter of motivation, a CV, and 2 letters of recommendation.

Contact: [email protected]