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

At the Ellison Institute of Technology (EIT), we're on a mission to translate scientific discovery into real world impact. We bring together visionary scientists, technologists, engineers, researchers, educators and innovators to tackle humanity's greatest challenges in four transformative areas:

• Health, Medical Science & Generative Biology
• Food Security & Sustainable Agriculture
• Climate Change & Managing CO₂
• Artificial Intelligence & Robotics
  
  

This is ambitious work - work that demands curiosity, courage, and a relentless drive to make a difference. At EIT, you'll join a community built on excellence, innovation, tenacity, trust, and collaboration, where bold ideas become real-world breakthroughs. Together, we push boundaries, embrace complexity, and create solutions to scale ideas from lab to society. Explore more at www.eit.org.

Welcome to the Pathogen Program:

Within this ecosystem, the Pathogen Program exemplifies EIT's dedication to ground-breaking science. It seeks to transform pathogen risk management, detection and response by leveraging Whole Genome Sequencing (WGS)-based metagenomic and pathogen-specific analytical tools. The goal is to power metagenomic devices using long-read sequencing technologies by building a comprehensive database of pathogen information to inform response. The database will comprise >400,00 paired high-quality long read sequences with comprehensive antimicrobial susceptibility profiles. Enabled by Oracle Inc.'s cloud-computing scale and security, the Pathogen Program is advancing toward certified diagnostic tools for deployment in laboratories, hospitals, and public health organisations worldwide.

Your Role:

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.

Key Responsibilities:

• 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
  
  

Requirements

Requirements

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.

Essential Knowledge, Skills and Experience:

• 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)