Deep Learning AI Engineer / Bioinformatics - Expression of Interest

Overview

Expression of Interest for future opportunities in Deep Learning AI Bioinformatics

What if the work you did every day could impact the lives of people you know? Or all of humanity?

At Illumina, we are expanding access to genomic technology to realize health equity for billions of people around the world. Our efforts enable life-changing discoveries that are transforming human health through the early detection and diagnosis of diseases and new treatment options for patients. Working at Illumina means being part of something bigger than yourself. Every person, in every role, has the opportunity to make a difference. Surrounded by extraordinary people, inspiring leaders, and world changing projects, you will do more and become more than you ever thought possible.

Expression of Interest for future opportunities in Deep Learning AI Bioinformatics

Our Deep Learning, Research and Development Team are looking at growing the team in 2024 and we invite those with exceptional deep learning, AI, bioinformatics skills to apply as an expression of interest for future opportunities to work on the development of novel deep learning algorithms for deciphering the effects of genetic variants in the human genome.

Based in the UK, Cambridge, Hub and Hybrid, onsite or Remote

Position Summary

Hundreds of millions of human genomes and exomes are expected to be sequenced over the next decade, driven by steady innovations in sequencing technologies pioneered by Illumina. The enormous quantities of genomic data being generated by Illumina in collaboration with our partners worldwide represents a major opportunity to develop novel data-driven and artificial intelligence methods to extract clinically actionable information from the genome, and apply it towards improving human health.

To accelerate the adoption of clinical sequencing, Illumina is seeking expression of interests from world-class computational scientists to work on the development of novel deep learning algorithms for deciphering the effects of genetic variants in the human genome. Major aims would include modeling the effects of genetic variants on protein function, transcriptional regulation, and diagnosis of pathogenic variants in patients with cancer or rare genetic diseases.

A key objective is to publish research results in peer-reviewed journals to improve the accuracy, throughput, and reproducibility of genome interpretation, thereby removing barriers to clinical adoption of whole genome sequencing. In addition to strong analytical skills, this position will require a high degree of initiative, autonomy, and scientific collaboration.