Computational Materials Scientist / Computational Chemist

GREEN14•Stockholm, ME

8d•Onsite

About The Position

GREEN14 is seeking a computational materials scientist or computational chemist to bridge the gap between industrial process engineering and simulation modeling. The role focuses on understanding complex high-temperature processes by connecting experimental data with advanced simulation techniques, including multiphysics, AI, and surrogate modeling. The goal is to develop fast, trustworthy surrogate models that enable engineers to explore operating conditions, understand sensitivities, and optimize processes efficiently, ultimately answering the question: 'What should I change to get the product I want?' The company, GREEN14, started five years ago with a mission to develop emission-free production of critical raw materials using hydrogen plasma. They operate a full-scale plasma reactor in Stockholm and have developed capabilities in multiphysics simulation, internal solvers, AI, and surrogate modeling. Their ambition is to create a seamless connection between experiment, physics, chemistry, simulation, surrogate modeling, optimization, and back to experiment.

Requirements

PhD or equivalent depth of experience in computational chemistry, materials science, chemical engineering, physical chemistry, computational materials science, plasma-material interactions, or a related field.
Strong understanding of high-temperature thermodynamics, reaction kinetics, phase transformations, surface reactions, materials chemistry, plasma chemistry, or heat and mass transfer.
Comfort working with computational models and understanding their interaction with fluid flow, temperature, pressure, species transport, electromagnetic fields, and material properties.
Understanding that a beautiful model is not necessarily a useful model.
Care about assumptions, boundary conditions, validation, and whether predictions align with experimental data.
Genuine interest in industrial processes.
Ability to understand how something is made and identify real constraints.
Mindset to consider if a problem can be modeled, validated, if a surrogate can be built, and if solving it would matter.

Nice To Haves

Experience with multiphysics simulation
Experience with CFD
Experience with COMSOL
Experience with thermodynamic or kinetic modeling
Experience with CALPHAD
Experience with plasma modeling
Experience with molecular or atomistic modeling
Experience with Python
Experience with scientific computing

Responsibilities

Understand phenomena occurring inside a plasma reactor, particularly at the interface between high-energy plasma and materials.
Investigate extreme heating rates, reactive species, complex heat and mass transfer, chemical reactions, melting, evaporation, reduction, nucleation, and phase transformations.
Determine which phenomena control process outcomes.
Analyze particle heating rates, species transport to surfaces, thermodynamic and kinetic relevance of reactions, and material behavior during melting, reaction, evaporation, or cooling.
Decide which phenomena need explicit representation in models and which can be simplified.
Collaborate with process engineers to understand material behavior under different conditions.
Work with multiphysics engineers to represent phenomena computationally.
Identify and understand problems faced by process engineers in plasma, metallurgy, and other high-temperature industries.
Determine where GREEN14's technology can create value by combining multiphysics, experimental data, and AI into fast surrogate models.
Meet with process engineers, researchers, and industrial partners to understand their processes and identify valuable problems to solve.
Collaborate with process engineers, multiphysics engineers, AI specialists, and the scientific computing team.
Decide which chemical and material phenomena are important, how they should be represented computationally, and which assumptions are reasonable.
Determine necessary experiments for model validation.
Influence experimental design to gather crucial data.
Help understand and model physics or chemistry not captured by existing models in industrial processes.