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2024 Summer Intern - Biology Research | Ai Development - High - Content Perturbation Screens

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3mo ago

🚀 Summer Internship

San Francisco

⌛ Closed
Applications are closed

Summer Internship

Healthcare, ScienceSan Francisco


  • Welcome to the Biology Research | AI Development (BRAID) department, a part of Genentech Computational Sciences (gCS). Our mission is to advance biological discovery by developing AI/ML methods focused on target discovery from diverse data modalities generated within Genentech's Research Division. Aiming to revolutionize the interplay of technology and lab work, we're pioneering research on lab-in-the-loop projects where computational analysis guides our experimental design. Join us in transforming biological discovery by bridging biology, AI, and data science to accelerate drug discovery and improve human health.
  • The BRAID department is looking for a highly motivated Research Intern to improve algorithms for the analysis of large-scale perturbation screens, with a focus on representation learning and active learning for screens with transcriptomic and/or morphological readouts.


  • Required education:
  • Must be pursuing a PhD (enrolled student)
  • Required majors:
  • Computational Biology, Computer Science, Statistics, Mathematics, or similar quantitative or computational fields
  • Required skills:
  • Scientific programming experience in Python and familiarity with a deep learning framework such as PyTorch or JAX
  • Strong foundation in Data Science and Statistics
  • Preferred Qualifications:
  • Experience with active learning frameworks for experimental design
  • Familiarity with self-supervised representation learning methods, such as contrastive learning
  • Basic familiarity with transcriptomics data and the concepts of high-content perturbation screens such as Perturb-Seq
  • Excellent communication, collaboration, and interpersonal skills.
  • Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
  • Track record of tackling challenging biological problems with advanced computational methods

Education requirements

Currently Studying

Area of Responsibilities



  • Train ML models to learn representations of imaging and/or transcriptional readouts in the Python ecosystem (e.g., in PyTorch)
  • Evaluate learned representations for biological discovery, and measure the effect of confounders
  • Develop models to predict the outcome of unseen perturbations
  • Present on the scientific findings


Work type

Full time

Work mode



San Francisco