(Senior) Computational Genomics Scientist - Pathogen

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 Project:

Within this ecosystem, the Pathogen Project 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. Enabled by Oracle Inc.'s cloud-computing scale and security, the Pathogen Project is advancing toward certified diagnostic tools for deployment in laboratories, hospitals, and public health organisations worldwide.

Your Role:

At EIT we are seeking an experienced and detail orientated (Senior or Non Senior) Scientist with recognised expertise in developing computational methods and algorithms for genomic sequencing data analysis. This is an exciting opportunity to develop novel computational approaches for microbial data that will become part of our diagnostic products and thus help shape the future of infectious disease diagnostics. Reporting to the Lead Scientist in Computational Genomics, you will work closely with computational scientists, bioinformaticians, software engineers, and our database and data platform teams to deliver scientifically rigorous, scalable, and clinically relevant innovation in the microbial genomics space.

Key Responsibilities:

• Design, develop and evaluate novel computational methods in areas such as genome assembly, binning, and functional characterisation of genomes from metagenomic sequencing data
• Collaborate with scientists across EIT to apply the latest developments in AI/ML to real-world challenges in the pathogen space
• Work with bioinformaticians and software engineers to implement methods in scalable, reproducible, and modular workflows
• Perform rigorous benchmarking using public and internal datasets, and guide experimental validation efforts in collaboration with our wet-lab teams
• Communicate technical concepts to broad audiences and collaborate with interdisciplinary colleagues across the organization
  
  

Requirements

Requirements

The three core technical skillsets for this role are bioinformatics algorithm development, machine learning methods, and microbial genomics.

Strong candidates will have significant experience in at least two of these three areas, and experience in all three is a plus.

For Senior: At least three years of postdoctoral or industry experience developing computational methods for next-generation sequencing data, with the ability to plan and execute research projects independently.

For Non-Senior: Hands-on experience developing computational methods for next-generation sequencing data.

Essential Knowledge, Skills and Experience:

• PhD or equivalent experience in bioinformatics, computational biology, computer science, mathematics, physics, or another quantitative field
• Significant experience developing bioinformatics methods for efficient search and storage of DNA sequences, genome assembly, genome annotation, pangenomes, phylogenetics, and/or comparative genomics
• Solid understanding of data structures, algorithms, and statistical methods (e.g. De Bruijn graphs, hashing, Burrows-Wheeler transform, Bayesian methods, mixed models, dimensionality reduction, embeddings)