# Research Scientist, Generative Modelling for Materials and Chemistry

**Company**: Google DeepMind
**Location**: London, UK
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q15733006

**Apply**: https://job-boards.greenhouse.io/deepmind/jobs/7705247
**Canonical**: https://yubhub.co/jobs/job_994cf43a-1cf

## Description

At Google DeepMind, we're committed to equal employment opportunity and value diversity of experience, knowledge, backgrounds, and perspectives.

We're a multidisciplinary team building state-of-the-art generative models in chemistry & materials to accelerate scientific breakthroughs.

As a Research Scientist in our Science unit, you will be at the forefront of applying generative AI to the "Grand Challenge" of predicting the structure and properties of complex matter.

Your work will bridge the gap between in silico modeling and real-world laboratory discovery, particularly in areas where traditional computational methods are bottlenecked by time and complexity.

Key responsibilities:

- Design and train advanced AI models (transformers, generative models, etc.) to model the structure and properties of complex physical systems.

- Develop deep understanding of scientific domains that can be used to identify novel modelling approaches.

- Design and execute robust ML experiments that allow for the accumulation of small improvements, sharing results through clear verbal and written communication.

- Collaborate with other scientists and engineers to help shape the research roadmap.

About You

You are fascinated by the intersection of AI and natural science and determined to help solve grand challenges facing humanity.

In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:

- PhD / equivalent experience in computer science, computational chemistry, materials science, physics with a focus on atomistic simulation or structural biology.

- Fluency in generative models and transformers

- Excellent collaboration and communication skills

- Experience with modern deep learning frameworks

In addition, the following would be an advantage:

- Record of high-impact published work at the intersection of AI and natural science, particularly chemistry and materials science.

- Demonstrated experience in geometric deep learning.

Applications close on Monday 20th April at 5pm BST

## Skills

### Required
- PhD in computer science, computational chemistry, materials science, physics
- Generative models and transformers
- Collaboration and communication skills
- Modern deep learning frameworks
- Geometric deep learning