Fred Hutchinson Cancer Center is seeking a Post-Doctoral Research Fellow to develop and apply DNA and protein language models to understand and forecast viral evolution, aiming to improve predictions beyond traditional metrics.
Requirements
- A PhD in biology, computer science or a related field when starting the position is required.
- The ideal candidate will have experience working with deep learning models via PyTorch or other frameworks.
- Candidates with more traditional experience in sequence data and phylogenetic approaches who are excited to dive into deep learning models are also strongly encouraged to apply.
- Candidates should have experience in at least one programming language
- a proven track-record of peer reviewed publications.
Responsibilities
- In this role, you’ll initially focus on incorporating state-of-the-art language models to assess and predict the fitness of influenza and SARS-CoV-2 variants, comparing these predictions to our established statistical models.
- A key aim is to leverage these advanced models to provide deeper insights than traditional “mutational load” metrics, which simply count the number of amino acid changes.
- Additionally, you will explore how embedding spaces derived from these language models could offer new perspectives on evolutionary processes (see for an example of looking at semantic change via embedding).
- Beyond applying existing language model frameworks, you’ll have opportunities to design novel model architectures to describe the process of sequence evolution.
Other
- The candidate must be responsible, organized, and able to independently pursue research projects.
- cover letter that includes the names and contacts for three references and a short statement of research interests
- a current CV
- code samples or links to code on GitHub
