exaere logo

Computational Chemist

exaere
Full-time
Hybrid
Germany
Materials

About us

We are a Hamburg-based deep tech startup pioneering breakthrough technologies for critical metal separation and recovery. Our mission is to secure sustainable supply chains for strategic materials essential to the clean energy transition and strategic independence. By integrating quantum mechanical simulations, molecular dynamics and cutting-edge AI/ML approaches, we're developing next-generation coordination complexes with selectivity for critical metals.

The role

We are seeking an exceptional Computational Chemist to lead our molecular simulation efforts in designing and optimizing novel metal complexes in the field of critical metal binding. You will work at the intersection of quantum chemistry, molecular dynamics and machine learning to accelerate the discovery of unprecedented metal-ligand complexes in close collaboration with our lab team. This is a unique opportunity to directly impact global resource security while working with state-of-the-art computational methods and emerging AI technologies.

You’ll drive the development of our simulation infrastructure, establish best practices for high-throughput molecular modeling, and oversee the interface between computational predictions and experimental validation. If vision and interest are mutually aligned, a potential role within the founding team can be discussed.

What you will do 

  • Perform state of the art semi-empirical, DFT and ab initio calculations and simulations to predict binding energies, including both entropic and thermodynamic effects, electronic structure and coordination geometries 
  • Build and execute large-scale molecular dynamics simulations and or FEP based predictions 
  • Design, build and automate high-throughput docking and screening workflows to rapidly evaluate the conformational space of novel ligand systems 
  • Scale computational workflows on HPC clusters and GPU-accelerated systems, utilizing containerization techniques such as docker and parallel/distributed computing strategies
  • Benchmark your predictions with state of the art computational methods and data-driven approaches
  • Apply machine learning methods and employ cheminformatics tools in a predictive and generative manner 

Required qualifications

  • PhD in Computational Chemistry
  • Proven track record in molecular simulations, demonstrated by publication records and software contributions (e.g. git)
  • Strong foundation in quantum chemistry, statistical mechanics and computational methods
  • Excellent communication skills in English (written and spoken) - ability to explain complex computational results to diverse audiences
  • Thrives in fast-paced, dynamic startup environments with evolving priorities and rapid iteration cycles

    • Send your CV to yelda.demirdoegen@gmail.com