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Paid Internship
Work Mode
Time Spent
Required Degree
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22Open Positions

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Intern in Machine Learning for Antibody-Antigen Complex Scoring

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

Education

  • Master

Skills & Qualifications

Technical Skills

  • machine learning
  • geometric and spatial representation learning
  • PyTorch
  • Machine Learning
  • Structural Biology
  • computational techniques

Soft Skills

  • communication and collaboration skills
  • work effectively in international, cross-functional teams.
  • motivated
  • scientific rigor
  • unassailable ethics
  • access to medical innovations for all
  • courageous in both decision and action
  • good business means a better world
  • We are many, working as one across functions, across companies, and across the world

Job Description

AIDD (AI for Drug Discovery), part of Roche Computational Sciences Center of Excellence, is an interdisciplinary team applying machine learning to discover and engineer biologic drugs. Within the LMDD (Large Molecule Drug Design) team, we focus on developing next-generation therapeutics, particularly therapeutic antibodies, using innovative computational methods combined with biological expertise. Key areas include de novo design, structure prediction, affinity maturation, and accurate scoring of macromolecular complexes to enhance drug discovery capabilities. Develop a state-of-the-art multi-modal scoring function designed to bridge the gap between geometric deep learning and physics-based energy approximations. Take responsibility for creating and implementing innovative methods to rank generated antibody-antigen complexes. Collaborate closely with international, cross-functional teams across our key global sites in Basel, New York, and San Francisco. Drive high-level research initiatives with the clear goal of steering results toward a formal scientific publication. Act as a highly motivated intern within a fast-paced environment, merging advanced computational techniques with drug discovery efforts.

Requirements

  • Currently enrolled as a Master’s student in a technical field, such as Bioinformatics, Computer Science, Physics, or a related quantitative discipline.
  • Strong foundational knowledge of machine learning, with a specific focus on geometric and spatial representation learning.
  • Hands-on experience with modern deep learning frameworks, with a strong preference for PyTorch.
  • Familiarity with the application of Machine Learning within the field of Structural Biology.
  • Excellent communication and collaboration skills, with the ability to work effectively in international, cross-functional teams.
  • Proficiency in English, with strong verbal and written communication skills suitable for a professional and academic environment.

Related Field

  • AI & Machine Learning

Related Subfield

  • Applied Machine Learning

Languages

  • English

Nice to Haves

  • PyTorch
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