top of page

Oxford-based machine learning start-up Etcembly secures Private Seed Funding

Oxford, UK: 23rd April 2021. No embargo.


Etcembly Ltd (Etcembly), a company building a platform to decode immune repertoires in health and disease, has closed an oversubscribed seed round with a group of private investors.


The founders of Etcembly, Michelle Teng, CEO, and Jacob Hurst, CTO, bring a unique blend of machine learning and immunology expertise to create a next generation biotech.


Their goal is to build a pioneering machine learning platform that will accelerate the understanding of immune repertoires. The platform has been named EMLy™, which stands for Etcembly Machine Learning.


This successful seed round will support the first stages of validating and building EMLy™.

We are redefining how the science and machines interact to facilitate lean discovery and rapid deployment of personalised therapeutics,” explains Teng. “The need for accurate and accelerated discovery has been highlighted in the current pandemic climate. Our crusade is to understand the clonal behaviours of T cell repertoires and TCR specificity across all ethnicities, starting from a more diverse set of COVID-19 infected individuals across different ethnicities.”

The company is now looking to hire talented scientists and data engineers in this cross-disciplinary space.

“We are seeking exceptional individuals who possess the creativity to solve one of the greatest challenges in immunology.” clarifies Teng.

[ENDS]



About Etcembly (www.etcembly.com)


Etcembly is a privately owned, Oxfordshire-based company. Its mission is to decode immune repertoires in health and disease by building a pioneering machine learning platform (EMLy™). EMLy™ will be the world’s leading health and disease immune database to fulfil the vision of lean drug discovery for immunotherapy and personalised therapeutics across all patient groups globally.


Contact Information: please visit the website for more information or email hello@etcembly.com

Comments


bottom of page