Materials Researcher | Remote

Crossing Hurdles; United States; Remote; GBP 83,000 - 135,000; Part time

Job summary

Crossing Hurdles is seeking a Materials Science Expert for a remote, contract role. You will solve and validate computational materials-science problems, create material structures, and run simulations with Python.

Responsibilities include modeling composition-structure-performance relationships, analyzing properties, diagnosing calculation issues, and comparing results to data. Requires MS/PhD in a materials-related field and hands-on experience with LAMMPS, ASE, pymatgen, and Quantum ESPRESSO.

Qualifications

• An MS or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline.
• An MS or PhD in Mechanical Engineering or Chemical Engineering with a substantial materials specialization.
• Strong understanding of materials behavior and relevant structure-property relationships.
• Experience with computational materials modeling, simulation, characterization, or materials-focused engineering analysis.
• Practical proficiency with Python.
• Experience with at least one engineering or scientific tool that can be operated through a CLI, scripting interface, configuration files, or programmatic API.
• Ability to understand and justify modeling assumptions, parameters, approximations, and convergence criteria.
• Ability to distinguish computational failures from genuine physical behavior.
• Ability to explain complex scientific reasoning and technical limitations clearly.
• Experience with tools like LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or similar programmatic materials and simulation software.

Responsibilities

• Solve and validate computational materials-science and materials-engineering problems.
• Create material structures, atomic configurations, compositions, and solver-ready inputs.
• Model relationships between composition, structure, processing, properties, and performance.
• Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.
• Use Python to generate inputs, automate calculations, conduct parameter sweeps, process results, and validate outputs.
• Analyze mechanical, thermal, electrical, chemical, structural, or electrochemical properties.
• Diagnose failed calculations, invalid structures, convergence problems, numerical instability, and incorrect physical assumptions.
• Compare computational results with experimental data, literature values, known properties, or expected physical trends.
• Review AI-generated solutions for scientific correctness and identify invalid assumptions, configurations, or conclusions.
• Develop reproducible reference solutions and objective verification methods.

Skills

Python

Materials modeling

Structure-property relationships

Data analysis

Convergence criteria

Education

MS/PhD in Materials Science and Engineering

MS/PhD in Mechanical or Chemical Engineering with materials specialization

Tools

LAMMPS

ASE

pymatgen

Quantum ESPRESSO

FEniCSx

CalculiX

Elmer

PyBaMM

Job description

Position: Materials Science Expert

Type: Contract

Compensation: $80 - $130/hour

Location: Remote

Commitment: 10-40 hrs/week

• Solve and validate computational materials-science and materials-engineering problems.
• Create material structures, atomic configurations, compositions, and solver-ready inputs.
• Model relationships between composition, structure, processing, properties, and performance.
• Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.
• Use Python to generate inputs, automate calculations, conduct parameter sweeps, process results, and validate outputs.
• Analyze mechanical, thermal, electrical, chemical, structural, or electrochemical properties.
• Diagnose failed calculations, invalid structures, convergence problems, numerical instability, and incorrect physical assumptions.
• Compare computational results with experimental data, literature values, known properties, or expected physical trends.
• Review AI-generated solutions for scientific correctness and identify invalid assumptions, configurations, or conclusions.
• Develop reproducible reference solutions and objective verification methods.

Role Responsibilities

• Solve and validate computational materials-science and materials-engineering problems.
• Create material structures, atomic configurations, compositions, and solver-ready inputs.
• Model relationships between composition, structure, processing, properties, and performance.
• Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.
• Use Python to generate inputs, automate calculations, conduct parameter sweeps, process results, and validate outputs.
• Analyze mechanical, thermal, electrical, chemical, structural, or electrochemical properties.
• Diagnose failed calculations, invalid structures, convergence problems, numerical instability, and incorrect physical assumptions.
• Compare computational results with experimental data, literature values, known properties, or expected physical trends.
• Review AI-generated solutions for scientific correctness and identify invalid assumptions, configurations, or conclusions.
• Develop reproducible reference solutions and objective verification methods.

Requirements

• An MS or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline.
• An MS or PhD in Mechanical Engineering or Chemical Engineering with a substantial materials specialization.
• Strong understanding of materials behavior and relevant structure-property relationships.
• Experience with computational materials modeling, simulation, characterization, or materials-focused engineering analysis.
• Practical proficiency with Python.
• Experience with at least one engineering or scientific tool that can be operated through a CLI, scripting interface, configuration files, or programmatic API.
• Ability to understand and justify modeling assumptions, parameters, approximations, and convergence criteria.
• Ability to distinguish computational failures from genuine physical behavior.
• Ability to explain complex scientific reasoning and technical limitations clearly.
• Experience with tools like LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or similar programmatic materials and simulation software.