Intern - Physical Sciences

Spring 2026 Co-op/Internship OpportunityWe’re looking for motivated students to join our team starting in January/February 2026 for a spring co-op or internship. This is a great opportunity to gain hands-on experience, contribute to meaningful projects, and develop your skills in a fast-paced, collaborative environment. If you’re eager to learn and make an impact, we’d love to hear from you!

Company Summary

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.

Your Impact at Lila

As a PhD Co-op in Catalysis and Porous Materials, you’ll join Lila’s Physical Sciences team to explore next-generation materials for catalytic and sorption applications. You will work closely with a cross-functional team to synthesize and characterize heterogeneous catalysts, metal-organic frameworks (MOFs), and other porous materials. You will also contribute to designing automated workflows that couple material discovery with machine learning to accelerate performance optimization. This is an opportunity to work at the frontier of materials science, automation, and AI-driven discovery.

What You'll Be Building

• Design and execute experiments to investigate heterogeneous catalytic and sorption processes
• Synthesize and characterize MOFs, zeolites, and other porous materials using techniques such as PXRD, BET, TGA, and SEM
• Analyze experimental data and contribute to hypotheses and structure-property relationship models
• Collaborate with automation and ML teams to integrate high-throughput screening workflows
• Operate or support high-throughput platforms such as Chemspeed, Opentrons, or Unchained Labs systems for automated synthesis
• Document protocols, maintain accurate lab records, and present findings to technical stakeholders 

What You’ll Need to Succeed

• Current PhD student in Chemistry, Chemical Engineering, Materials Science, or related field
• Research experience with heterogeneous catalysts, MOFs, or porous materials
• Proficiency with materials synthesis and characterization (e.g., PXRD, BET, SEM, TGA, FTIR)
• Strong analytical skills and attention to detail in experimental design and data interpretation
• Excellent communication and teamwork skills 

Bonus Points For

• Experience with gas sorption, breakthrough experiments, or dynamic adsorption studies
• Familiarity with flow reactors or automated material testing setups
• Exposure to high-throughput experimentation and lab automation platforms
• Programming or data analysis skills (e.g., Python, MATLAB, JMP)
• Interest in machine learning approaches to materials optimization