Magnetic Materials MLIP Scientist (GNNs)

Magnetic Materials MLIP Scientist (GNNs)

60000 - 80000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Train and fine-tune machine-learning models for magnetic materials with cutting-edge techniques.
  • Company: Diffractive Labs, a pioneering tech firm in Greater London.
  • Benefits: Competitive salary, equity options, and opportunities for professional growth.
  • Other info: Collaborative environment with exciting projects and career advancement potential.
  • Why this job: Join a team of innovators and make a real impact in materials science.
  • Qualifications: PhD in a relevant field with experience in machine learning and training dynamics.

The predicted salary is between 60000 - 80000 Β£ per year.

Diffractive Labs in Greater London is seeking a Machine Learning Engineer to specialize in training and fine-tuning machine-learning interatomic potentials for magnetic and structural materials. You will collaborate with materials scientists and leverage DFT datasets to develop innovative MLIP architectures.

The ideal candidate holds a PhD in a relevant field and has proven experience in ML and training dynamics. Benefits include a competitive salary and equity.

Magnetic Materials MLIP Scientist (GNNs) employer: Diffractive Labs

At Diffractive, we pride ourselves on being an exceptional employer, offering a unique opportunity for a DevOps Engineer to shape the future of AI-driven scientific discovery. Our London-based team is composed of world-class engineers and researchers, fostering a collaborative and fast-paced work culture that values innovation and inclusivity. With competitive salaries, generous equity options, and a commitment to employee growth, you'll have the chance to make a meaningful impact while enjoying a flexible work environment.

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Contact Details:

Diffractive Labs Recruitment Team

We think you need these skills to ace Magnetic Materials MLIP Scientist (GNNs)

Problem-Solving Skills
Communication Skills
Python
SQL
Data Engineering
Attention to Detail
Data Pipeline Development