Molecular AI ML Scientist — Graph & Protein Discovery

Molecular AI ML Scientist — Graph & Protein Discovery

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

  • Tasks: Develop AI methods for molecular and protein discovery, translating ideas into practical tools.
  • Company: Nxera Pharma, a leader in innovative scientific research.
  • Benefits: Competitive salary, health benefits, and opportunities for publishing your work.
  • Other info: Collaborative environment with a focus on innovation and career growth.
  • Why this job: Join a cutting-edge team and make a real impact in scientific discovery.
  • Qualifications: Experience in machine learning, particularly with graph neural networks and deep learning.

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

Nxera Pharma is seeking an exceptional Machine Learning Scientist to develop next‑generation AI methods for molecular and protein discovery.

You will publish and translate ideas into practical tools that accelerate scientific discovery.

We value strong scientific contributions, with a focus on graph neural networks, transformers, diffusion models, and geometric deep learning.

Collaboration with engineers to deploy research is essential. #J-18808-Ljbffr

Molecular AI ML Scientist — Graph & Protein Discovery employer: Nxera Pharma

Join a pioneering team at the forefront of AI-driven molecular discovery, where your contributions will directly impact scientific advancements. Our collaborative work culture fosters innovation and encourages continuous learning, providing ample opportunities for professional growth and development. Located in a vibrant tech hub, we offer a dynamic environment that values creativity and excellence, making it an ideal place for passionate researchers to thrive.

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Contact Details:

Nxera Pharma Recruitment Team

We think you need these skills to ace Molecular AI ML Scientist — Graph & Protein Discovery

Machine Learning
Graph Neural Networks
Transformers
Diffusion Models
Geometric Deep Learning
Scientific Research
Tool Development