We are seeking a highly skilled Computational Modeller to join our interdisciplinary team. As a key member, you will contribute to cutting-edge projects in computational chemistry and materials science.
About the Role:
You will be responsible for developing and optimizing computational workflows integrating quantum simulations, molecular descriptors, and predictive machine learning models. Your tasks will include:
• Developing advanced cheminformatics and data-driven approaches for virtual screening, property prediction, and reaction mechanism analysis
• Designing and implementing automated pipelines for high-throughput quantum chemical calculations and machine learning model training
• Modelling and simulating complex chemical reaction mechanisms across diverse environments using quantum mechanical and ML-assisted approaches
• Analyzing simulation results to extract molecular-level insights and support experimental design or product development
• Conducting comprehensive literature reviews and generating high-quality technical documentation, reports, and presentations
Requirements:
We require a strong academic background in quantum chemistry, computational chemistry, cheminformatics, materials science, physics, or a closely related field. You should have hands-on experience with quantum chemistry methods (DFT, ab initio) and related software (Gaussian, ORCA, VASP, CP2K, DMol³). Additionally, you should possess a strong background in cheminformatics, including experience with molecular representations and libraries such as RDKit and Open Babel. Experience with machine learning (scikit-learn, PyTorch, TensorFlow) is also essential. Familiarity with molecular dynamics tools (GROMACS, LAMMPS) is a plus. A postdoctoral or several years of industrial experience in the field is highly preferred. Excellent written and spoken English communication skills are required, along with the ability to articulate complex technical concepts to diverse audiences.