Senior) Computational Genomics Scientist, Algorithms & AI​/ML

EIT - Ellison Institute of Technology

Oxford, United Kingdom

As a (Senior) Scientist, you will design, develop, and evaluate novel computational methods for translating metagenomic sequencing data into actionable results in a clinical or public health context.

You will develop algorithms and AI/ML methods that are accurate and robust enough to be integrated into our infectious disease diagnostic product. You will report to the Lead Scientist in Computational Genomics, and you will work closely with bioinformaticians, data scientists, microbiologists, and software engineers to deliver clinically relevant innovation in the microbial genomics space.

• Design, develop, and evaluate novel computational algorithms in areas such as taxonomic classification and antimicrobial resistance
• Develop ML/AI techniques to address real-world problems in the pathogen genomics space, in collaboration with scientists across EIT
• Validate and benchmark methods using public and internal datasets
• Work with bioinformaticians and software engineers to implement methods in scalable, reproducible, and modular workflows
• Communicate technical work clearly to interdisciplinary teams

This is a methods development role. Strong candidates will have deep expertise in either bioinformatics algorithm development or ML/AI in a genomics context. Depth in both areas is a plus.

Experience with microbial genomics and metagenomics is preferred but not required. We actively welcome applicants with genomics experience in other contexts who are interested in applying their methods development expertise in the pathogens space.

For Senior:
Candidates will have a track record of independently executed research projects, typically with three or more years of experience in industry or as a postdoc.

• PhD or equivalent experience in a quantitative field, such as computational biology, computer science, mathematics, or physics
• Track record of developing computational algorithms or tools in the genomics space. Algorithms:
  Designed and released a tool in a relevant area, such as sequence search/storage, genome assembly/annotation, pangenome graphs, or phylogenetics. AI/ML:
  Ownership of model training and evaluation at scale, with a deep understanding of the underlying methods and architectures
• Strong command of underlying fundamental data structures, algorithms, and statistical methods (e.g. De Bruijn graphs, embeddings, Bayesian methods)
• Solid programming skills (e.g. Python; C, C++, or Rust is a plus) and software development best practices (version control, CI/CD)

• Experience in microbial genomics
• Experience with long-read sequencing data (ONT)
• Experience with cloud computing, workflow management, and ML ops
• For senior:
  Experience mentoring or line-managing scientists