Machine Learning Materials Researcher

IXO Inc.; France; Remote; EUR 3,600 - 8,900; Full time

Job summary

IXO Inc. seeks a collaborator to develop predictive models and reproducible workflows for computational materials research.

You will apply ML to material discovery and property prediction, training and evaluating models with DFT, MD, or Monte Carlo outputs, and integrating them into high-throughput pipelines. The role requires a strong ML and materials science background, a master’s or PhD, and the ability to solve problems independently.

Qualifications

• Peer-reviewed publication
• Expertise in ML and computational materials science
• Graduate qualification at master’s or doctoral level
• Independent technical problem-solving ability

Responsibilities

• Develop predictive ML models for material discovery and property prediction
• Train and evaluate models using DFT, MD or Monte Carlo outputs
• Integrate approaches into high-throughput materials pipelines
• Explain modeling assumptions and document reproducible methods

Skills

ML for materials

DFT/atomistic simulations

Graph neural networks

Python

Education

Master’s or PhD in materials science or related field

Tools

VASP

Quantum ESPRESSO

LAMMPS

ASE

Job description

Develop predictive models and reproducible workflows for computational materials research.

What you will do

• Apply machine learning to material discovery, property prediction and structure–property questions.
• Train and evaluate models using DFT, molecular-dynamics or Monte Carlo outputs.
• Use supervised and unsupervised approaches and integrate them into high-throughput materials pipelines.
• Explain modeling assumptions and document reproducible methods.

Required background

• At least one first-author publication in a peer-reviewed journal.
• Expertise in both ML and computational materials science, such as DFT, force fields, atomistic simulation or materials informatics.
• A graduate qualification at master’s or doctoral level in materials science, computational chemistry, physics, computer science or another relevant quantitative subject.
• Independent technical problem-solving ability.

Preferred background

• Teaching or TA work in computational physics, chemistry or materials science.
• CGCNN, MatGL, M3GNet, ALIGNN or related graph-network experience.
• VASP, Quantum ESPRESSO, LAMMPS, ASE or comparable materials tools.

Availability

• Remote and asynchronous; plan around 10–20 hours weekly for two to three weeks, with the scope and dates agreed before work.

Pay and engagement

Per project; fee agreed before work.

The agreed project fee determines payment. Any hourly equivalent is an estimate, not an hourly-pay commitment.

Amounts are in USD. The applicable rate or fee and work requirements are agreed before you begin.