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- Tasks: Lead the design and management of a cutting-edge materials lab, executing innovative experiments.
- Company: Join Google DeepMind, a leader in AI, dedicated to advancing science for public benefit.
- Benefits: Enjoy a collaborative environment, competitive salary, and opportunities for professional growth.
- Why this job: Be at the forefront of scientific discovery, making a real impact on global challenges.
- Qualifications: PhD in Materials Science or related field, with hands-on experience in solid-state synthesis.
- Other info: Diversity is valued; we encourage applicants from all backgrounds to apply.
The predicted salary is between 36000 - 60000 £ per year.
Science is at the heart of everything we do at Google DeepMind. From the beginning, we took inspiration from science to build better algorithms, and now, we want to use our toolkit to accelerate scientific discovery. By bringing together specialists with backgrounds in machine learning, computer science, physics, chemistry, biology and more, we’re optimistic that we can build new methods that will push the boundaries of what is possible and help solve the biggest problems facing humanity.
About us
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
The role
Google DeepMind (GDM) is pursuing a ground-breaking research program in materials, aiming to accelerate the discovery of new functional materials by combining the predictive power of artificial intelligence (AI) with automated experimentation. The team is establishing experimental capacity to create a closed-loop, AI-driven discovery engine. This lab will be crucial for synthesizing and characterizing novel materials, validating AI-generated hypotheses, and generating high-quality data to refine our models.
We are seeking an exceptional and highly motivated expert in solid-state synthesis and characterization to lead the design, outfitting, and management of this new laboratory. This is a founding role with a unique blend of scientific leadership, hands-on experimental work, and strategic input. You will be instrumental in building our experimental capabilities from the ground up and refining the critical in-silico to experiment feedback loop that is at the heart of our mission.
Key responsibilities
- Lab Design & Setup: Lead the strategic planning, design, equipment selection, and commissioning of a new materials synthesis and characterization laboratory.
- Experimental Execution & Leadership: Independently plan and execute experimental workflows to synthesize and characterize novel inorganic materials proposed by our AI models.
- Workflow Automation: Collaborate with engineers and AI researchers to develop and implement high-throughput and automated experimental workflows, from precursor handling to data analysis.
- Data Integrity & Feedback Loop: Ensure the generation of high-quality, reproducible experimental data. Play a key role in structuring and feeding this data back to the AI team to create a rapid and effective discovery cycle.
- Lab Management, Health & Safety: Establish the operations management structure and working practices of the lab, including instrument maintenance, administration of consumable inventory, and establishing and enforcing comprehensive health and safety protocols.
- Cross-functional Collaboration: Work closely with AI researchers, computational scientists, and software engineers to translate AI-generated hypotheses into tangible experiments and to troubleshoot the sim-to-real gap.
- Reporting & Communication: Clearly and efficiently report on experimental progress, findings, and challenges to the wider Material Intelligence team and key stakeholders.
About you
In order to set you up for success as a Laboratory Research Scientist at Google DeepMind, we look for the following skills and experience:
- Deep, recognized expertise in materials synthesis methodologies (e.g., solid state synthesis, thin-film deposition or combinatorial methods).
- Extensive hands-on experience and a significant track record of synthesizing and discovering novel materials in a laboratory setting,
- Strong conceptual understanding of laboratory automation, robotics, and high-throughput experimental workflows.
- Comprehensive knowledge of a wide array of material characterization and measurement techniques
- Proven experience in setting up, equipping, and commissioning new laboratory spaces or significant experimental capabilities
- Strong laboratory management skills
Required:
- PhD and post-PhD experience in Materials Science, Solid-State Chemistry, Condensed Matter Physics, or a related field.
- Deep, recognized expertise and extensive hands-on experience in solid-state synthesis methodologies.
- Proven, hands-on proficiency with a wide array of material characterization and property measurement techniques (e.g., Powder XRD, SEM/EDS, thermal analysis, relevant electrical/magnetic property measurements).
- Proven experience in setting up, equipping, and commissioning new laboratory spaces or significant experimental capabilities.
- Demonstrated ability to independently lead and manage complex experimental research projects, from conception to data analysis and publication/patenting.
- High attention to detail and a history of persistence in troubleshooting complex experimental challenges to produce high-quality data.
- Excellent teamwork and communication skills, with experience in interdisciplinary collaboration between experimental and computational/theory groups.
In addition, the following would be an advantage:
- Expertise in one or more advanced synthesis techniques (e.g., thin-film deposition (PVD/CVD), combinatorial methods, high-pressure synthesis).
- Strong conceptual and/or practical experience with laboratory automation, robotics, and high-throughput experimental workflows.
- Experience in laboratory management, including budgeting, procurement, and ensuring compliance with health & safety requirements.
- Excellent lab operations and stakeholder management, including experience managing external relationships with vendors, equipment suppliers and contractors.
- A significant track record of high-impact research in materials science or related areas.
- Experience hiring and mentoring junior researchers.
- Familiarity with programming (e.g., Python) for instrument control or custom data analysis.
At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know
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Materials Laboratory Scientist employer: Google DeepMind
Contact Detail:
Google DeepMind Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Materials Laboratory Scientist
✨Tip Number 1
Familiarise yourself with the latest advancements in materials synthesis and characterisation techniques. Being well-versed in cutting-edge methodologies will not only boost your confidence but also demonstrate your commitment to the field during discussions.
✨Tip Number 2
Network with professionals in the materials science community, especially those involved in AI-driven research. Attend relevant conferences or webinars to connect with potential colleagues and learn about their work, which could give you insights into the role at Google DeepMind.
✨Tip Number 3
Prepare to discuss your previous experiences in laboratory management and automation. Highlight specific projects where you successfully set up new lab spaces or implemented high-throughput workflows, as these are crucial for the role.
✨Tip Number 4
Showcase your interdisciplinary collaboration skills by preparing examples of how you've worked with computational scientists or engineers in the past. This will illustrate your ability to bridge the gap between experimental and theoretical work, a key aspect of the position.
We think you need these skills to ace Materials Laboratory Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your expertise in materials synthesis methodologies and any relevant hands-on experience. Use specific examples of your work with solid-state synthesis and characterization techniques to demonstrate your qualifications.
Craft a Compelling Cover Letter: In your cover letter, express your passion for advancing scientific discovery through AI and how your background aligns with Google DeepMind's mission. Mention specific projects or experiences that showcase your leadership in laboratory settings.
Highlight Interdisciplinary Collaboration: Emphasise your experience working with cross-functional teams, particularly in translating AI-generated hypotheses into experiments. This will show your ability to collaborate effectively with AI researchers and computational scientists.
Showcase Problem-Solving Skills: Provide examples of complex experimental challenges you've faced and how you overcame them. Highlight your attention to detail and persistence in troubleshooting to produce high-quality data, which is crucial for this role.
How to prepare for a job interview at Google DeepMind
✨Showcase Your Expertise
Make sure to highlight your deep expertise in materials synthesis methodologies during the interview. Be prepared to discuss specific techniques you've mastered, such as solid-state synthesis or thin-film deposition, and provide examples of how you've applied them in past projects.
✨Demonstrate Problem-Solving Skills
Expect to be asked about complex experimental challenges you've faced. Prepare to share detailed stories that illustrate your persistence and attention to detail in troubleshooting these issues, as this will showcase your ability to produce high-quality data.
✨Emphasise Collaboration Experience
Since the role involves cross-functional collaboration, be ready to discuss your experience working with interdisciplinary teams. Highlight any successful projects where you collaborated with AI researchers or computational scientists to translate hypotheses into experiments.
✨Prepare for Technical Questions
Brush up on your knowledge of laboratory automation and high-throughput workflows, as these are crucial for the role. Be prepared to answer technical questions related to equipment selection, lab design, and data integrity to demonstrate your comprehensive understanding of the field.
