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Graduate Intern - Machine Learning/AI Guided Antibody Design

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1mo ago

ūüöÄ Off-cycle Internship


AI generated summary

  • You should have a graduate-level degree in a relevant field, experience in machine learning, deep learning model development, Python fluency, AWS knowledge, and hands-on protein/Ab design using AI algorithms.
  • You will develop ML programs for antibody design, optimize strategies for predictability, collaborate with wet-lab scientists, and engage in design-build-test cycles for functional antibodies.

Off-cycle Internship

Data, Design‚ÄĘMinneapolis


  • All internship positions are designed to give college students an opportunity to apply techniques learned in an academic setting while obtaining new skills. This is a paid internship offering full-time hours during the summer months. All interns are required to conclude the program by giving a formal presentation on their work. Please note that no relocation assistance or sponsorship is provided for the internship program at this time.
  • Machine Learning/AI Guided Antibody Design: The graduate internship is an excellent opportunity for to develop novel antibodies using machine learning and AI based tools. This position will involve tuning existing ML programs for Ab design so that wet lab data can be integrated for developing precisely engineered antibodies with desired properties.


  • Program Requirements:
  • Must be a currently enrolled student pursuing a graduate-level degree in a field relevant to the internship.
  • Must be able to work full-time during the duration of the internship program.
  • Experience Qualifications:
  • Strong understanding of statistics and machine learning fundamentals
  • Practical experience developing deep learning models from scratch, and tuning existing ones
  • Fluency in Python and commonly used higher-level language for model training
  • Fluency with Unix environments, AWS, and GitHub
  • Hands-on experience in protein/Ab design by applying machine learning and AI algorithms

Education requirements

Currently Studying

Area of Responsibilities



  • Develop novel strategies and optimize existing ML programs to enable input of wet lab data for better predictability.
  • In collaboration with our wet-lab scientists use design-build-test cycles to develop functional antibodies


Work type

Full time

Work mode