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Lila Sciences

AI-powered scientific discovery for medicines

Electron Microscopy Co-op

Physical SciencesConfirmed live in the last 24 hours
InternshipCambridge, MA, USANo salary listedIn-person

About the Job

Requirements

  • Currently enrolled in a PhD program in Materials Science, Physics, or a related field.
  • Have a strong background in scanning electron microscopy-based microstructural analysis.
  • Have hands-on expertise with energy-dispersive X-ray spectroscopy, electron backscatter diffraction, and focused ion beam techniques.
  • Have experience characterizing powdered materials and thin films.
  • Demonstrate the ability to operate and troubleshoot advanced scanning electron microscopy systems.
  • Have strong organizational skills and attention to detail.

Responsibilities

  • Design and execute scanning electron microscopy-based characterization workflows across diverse chemistries and form factors.
  • Operate electron microscopy instrumentation under the guidance of staff scientists.
  • Partner with experimental scientists and engineers to optimize protocols and inform downstream analysis.
  • Troubleshoot instrumentation and workflow issues that affect data quality.
  • Capture imaging and analysis results in clear, reusable laboratory records.
  • Follow safety and compliance standards for instrument and sample handling.

Desired Qualifications

  • Have experience with image analysis tools such as ImageJ or Python-based pipelines.
  • Be familiar with complementary techniques such as X-ray diffraction or X-ray fluorescence.
  • Have interest in automation and integration of microscopy with artificial intelligence workflows.
  • Have prior experience characterizing nanoscale defects and interfaces.

About the Company

Lila Sciences

AI-powered scientific discovery for medicines

Lila.ai uses artificial intelligence to speed up scientific discovery in medicines and advanced materials. The system ingests large sets of scientific data, experimental results, and literature to identify patterns, generate hypotheses, and guide research programs. It differentiates itself by tackling deep scientific problems at scale and combining AI with domain expertise, rather than offering generic tools. The goal is to help humanity by delivering new medicines and materials that solve major biological and materials challenges through data-driven discovery.

Company size
501-1,000
Company stage
Series A
Total funding
$550M
Headquarters
Cambridge, MA
Founded
2024

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