.Job title: Associate Director, AI Scientist - Machine Learning for Biologics Engineering Location: Barcelona (Spain), Toronto (Canada), Gothenburg (Sweden), Cambridge (UK), Gaithersburg (USA) Make a more meaningful impact on patients' lives around the globe.
Here you'll have the opportunity to make a meaningful difference to patients' lives.
With science at its heart, this is the place where breakthroughs born in the lab become transformative medicines - for the world's most complex diseases.
Answer unmet medical needs by pioneering the next wave of science, focusing on outcomes and shaping the patient ecosystem.
With our ground-breaking pipeline, the outlook is bright.
Be proud to be part of a place that has achieved so much, yet is still moving forward.
There's no better time to join our global, growing enterprise as we lead the way for healthcare and society.
If you would like to have an impact on transforming patients' lives by accelerating new medicines to patients, this may be the position for you!
What you'll do You will be working on the design and development of a cyclic discovery process for biologics engineering based on active learning/optimization/search (machine learning models inform the design of wet-lab experiments, the wet-lab automation generates new high-throughput data that is used for model re-training and update of the hypothesis informing the next design step), as well as development of deep learning algorithms for virtual screening of antibodies (supporting the efforts for in silico lead identification and de novo design of antibodies).
You will design, implement, test, and analyze machine learning algorithms to help contribute to the overall improvement and automation of the pipeline for biologics engineering.
You will be required to interact extensively with other teams across the organization, and our academic partners with the goal of delivering products in a timely manner.
It is expected that you will present to various partners, represent us at conferences, and publish your findings in scientific journals or top conferences such as ICML, ICLR, NeurIPS, etc.
Accountabilities Work efficiently in a team of 3-10 (led by a Senior AI Researcher) to optimally deliver projects using the latest ML methods and modern engineering standard processes.
Work multi-functionally with our partners from Biologics Engineering to develop machine learning methods for the design of antibodies with improved biological activity.
Analyze challenges from the drug development process and provide creative solutions from the fields of representation learning, reinforcement learning, active learning, statistical learning theory, causality, ranking and recommendation.
Remain at the forefront of ML Research by participating in journal clubs, mentoring, contributing publications and personal development projects