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Paid Internship
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12Open Positions

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Internship: Agentic Workflows for Single-Cell RNA-seq Based Target Discovery

Roche
Found 3 weeks ago
Location
Basel, Switzerland
Duration (Months)
6 Months
Time
Not disclosed
Work Mode
Not disclosed
Salary
Not disclosed
Visa Help
Not disclosed
Last Verified
3 weeks ago

Education

  • Master

Skills & Qualifications

Technical Skills

  • Python
  • R
  • Git
  • CI/CD pipelines
  • single-cell RNA-seq analysis
  • machine learning (ML)

Soft Skills

  • Excellent communication skills
  • passion for interdisciplinary collaboration

Job Description

We seek an exceptional individual for a 6-month internship based in Basel or Zurich, Switzerland. As part of the Computational Sciences Center of Excellence (CS CoE), you will contribute to enhancing research automation processes and cutting-edge agentic workflows supporting target identification in oncology. Your primary goal will be to identify gaps in our data and analysis ecosystem by collaborating with computational scientists working on target identification. You will evaluate new agentic workflows against existing tools and expert outputs, then partner with machine learning (ML) researchers and data experts to address key gaps. This may involve integrating new datasets, improving models, or developing complementary workflows. As a Computational Biology and Medicine (CBM) intern within Roche’s strategic CS CoE team, you will interact with experts in computational biology, ML research, and data engineering. Your work will span the strategic intersection of biology and translational science, directly contributing to Roche’s mission to improve healthcare.

Requirements

  • currently enrolled in a Master’s degree program in a relevant field (e.g., Computational Biology, Bioinformatics, Data Engineering, Computer Science, etc.)
  • Practical experience in single-cell RNA-seq analysis, including preprocessing techniques
  • Experience in systematically comparing tools or methods
  • Strong programming skills in Python and/or R and experience working with large-scale datasets
  • Familiarity with collaborative workflows, including proficiency in Git, CI/CD pipelines, code review processes, and reproducible analysis documentation
  • Excellent communication skills, with a passion for interdisciplinary collaboration
  • Proficiency in English (spoken and written)

Related Field

  • AI & Machine Learning

Related Subfield

  • AI Research

Languages

  • English

Nice to Haves

  • references
  • list of relevant publications
▶Apply Now

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