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Internship

Research Intern in Science (Machine Learning for Biology)

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InstaDeep

•

Oct 11

🚀 Off-cycle Internship

London

⌛ Closed
Applications are closed

Off-cycle Internship

Research & Development, Science•London

Description

  • Join us as we continue on our journey to transform industries, push the boundaries of AI innovation, and deliver unparalleled value to our clients. 

Requirements

  • Currently enrolled in a PhD programme (or recent graduate) in a related STEM discipline.
  • Theoretical and practical knowledge in machine learning and deep learning, including experience with a deep learning framework such as Jax, PyTorch or Tensorflow.
  • Excellent communication skills and collaborative spirit.
  • Relevant experience in the application of deep learning to life science applications is highly desirable. Specific experience in any of the following domains is a plus but not essential;
  • Structural biology (e.g. protein structure prediction)
  • Protein language models
  • Drug discovery
  • Molecular dynamics
  • Protein-protein interactions
  • Methods for handling 3D structural data, such as Graph Neural Networks.
  • Generative ML models
  • Work permit for the UK for the duration of the internship.

Education requirements

Currently Studying
STEM
PhD

Area of Responsibilities

Research & Development
Science

Responsibilities

  • Support the efforts of the Science team through the development of novel methods and applications under the guidance of our Research Scientists and Engineers.
  • Design and implement experiments for proof of concept and benchmarking.
  • Contribute to team research and publications.
  • Report and present experimental results and research findings, both internally and externally, verbally and in writing.
  • Upon request, collaborate with other groups’ activities, including but not limited to presenting the company to new prospective clients, participating in calls and meetings, and representing InstaDeep in conferences/events.

Details

Work type

Full time

Work mode

hybrid

Location

London