CompBio Machine Learning Intern (Optimisation)

Responsibilities

• Benchmark, optimise and fine-tune state-of-the-art protein structure prediction models related to immune recognition.
• Evaluate different schemes for model scoring and assess their effectiveness in various use cases.
• Optimise and work with different architectures for tasks related to protein structures, such as Graph Neural Networks (GNNs) and Transformers, incorporating multiple modalities including sequence, structure and noisy experimental data.
• Report and present experimental results and research findings, both internally and externally, verbally and in writing.
• Implement successful algorithms in Python production code, following software engineering best practices.

Requirements

• Theoretical and practical knowledge of deep learning and machine learning algorithms, as well as numerical optimisation methods.
• Experience of at least one deep learning framework such as JAX, PyTorch and/or Tensorflow.
• Understanding of how to work with sequence and structure data, including preprocessing, representation and integration into ML models.
• Software development skills in Python.
• Previous exposure to research through internships or study projects.
• Excellent communication skills and collaborative spirit.

Desirables

• Currently in the process of completing an MSc or PhD in computer science, computational biology, applied mathematics or a related field.
• Demonstrated ability to deliver high-quality research through publications, conference presentations, technical blog posts or code repositories.
• Familiarity with ML approaches to biology, in particular applied to molecular or structural biology.