Computational Chemist

Join Paebbl, a founder-led start-up in the burgeoning field of climate technology and the emerging “carbon economy”. Our mission is to permanently sequester CO2 and repurpose it into essential products. Inspired by natural CO2 mineralization, our technology accelerates this process by a factor of a million and is now ready for scale-up. We are backed by supportive, long-term oriented institutional and private investors, and our team features a diverse group of experts from technology, industrial engineering, digital innovation, waste management, finance, and academia.

Exciting Opportunity for a Computational Chemist:
We are seeking a highly motivated Computational Chemist to advance our understanding and optimization of CO mineralization processes. This role will focus on modeling molecular interactions, reaction dynamics, and surface adsorption mechanisms on silicate minerals, with an emphasis on liquid-phase systems. Your work will integrate computational techniques with experimental feedback, driving innovation in carbon capture and storage technologies.

• Quantum Simulations: Perform advanced simulations for mineral systems, including Density Functional Theory (DFT) calculations using tools such as Quantum ESPRESSO and ORCA, to optimize surface reactivity for CO mineralization.
• Liquid-Phase Modeling: Investigate variables like pH, ionic concentration, and solution composition, simulating their influence on mineral dissolution and reactivity.
• Molecular Dynamics (MD): Explore the temperature- and pressure-dependent behaviors of reaction intermediates and products in solution and at interfaces.
• Hybrid Methods Development: Apply QM/MM approaches to capture molecular precision and macroscopic trends in complex systems.
• Additive Design Insights: Provide molecular-level insights into additive interactions to guide experimental formulation strategies.
• Experimental Collaboration: Partner closely with laboratory teams to validate computational predictions and refine simulations based on experimental data.

1. Education: PhD in Computational Chemistry, Materials Science, Geochemistry, or a related field.
2. Experience:
3. Minimum of 8 years of postdoctoral research or industry experience, with demonstrated expertise in solid–liquid systems, ideally involving silicate minerals or similar materials.
4. Proven experience in modeling CO–water interactions, preferably under high-pressure and high-temperature conditions.
5. Technical Proficiency:
   • Extensive experience with tools such as Quantum ESPRESSO, ORCA, or similar for DFT and TD-DFT calculations.
   • Proficiency in programming languages like Python, Fortran, or MATLAB.
   • Expertise in modeling ionic strength, solvation effects, and liquid-phase reaction dynamics.
   • Familiarity with hybrid QM/MM methods and machine learning techniques for accelerating simulations.
6. Skills: Strong analytical abilities, collaborative mindset, and a proactive approach to integrating computational data with experimental workflows.