.Are you passionate about creating artificial intelligence and machine learning models, algorithms, and tools for real-world science applications? Does contributing to preventing, modifying, and even curing some of the world's most complex diseases inspire you? Would you like to work on designing and developing an iterative drug discovery and development process while drawing on methods across various fields, from active learning to optimisation and search? What about advancing our understanding of biology, streamlining research and development processes, and leveraging a variety of data modalities? Do you thrive working in a supportive, inclusive environment where creativity, collaboration across disciplines and lifelong learning towards innovative breakthroughs are encouraged? If yes, this opportunity may be for you.Join our interdisciplinary Centre for Artificial Intelligence team in partnership with Biologics Engineering, which is responsible for discovering, designing, and... optimising the next-generation biological drug candidates across all key therapeutic areas at AstraZeneca. Your work will contribute to uncovering biological insights, automating processes, streamlining decisions, and improving the overall pipeline throughout the biologics engineering value chain.We have a variety of positions at different levels.Accountabilities:- You will work efficiently in a team to deliver projects optimally using the latest AI/ML methods, approaches, and techniques, with engineering best practices and standard processes.- You will be part of multifunctional teams, particularly with our partners from Biologics Engineering, to develop machine learning methods and tools for discovering, designing, and optimising large molecules such as antibodies with improved biological activity.- You will analyse challenges and opportunities in the drug discovery and development value chain processes and provide innovative solutions in fields such as deep learning, representation learning, reinforcement learning, meta-learning, active learning approaches applied to de novo protein design, protein engineering, in-silico discovery, structural biology, computational biology, and many other areas.- You will remain at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring, personal development initiatives, and contributing to publications and academic and industry collaborations.- A PhD in computer science, statistics, mathematics, physics, biology or related field OR MSc and proven experience developing artificial intelligence and machine learning models.- Experience with deep learning methods and their applications, particularly to problems in biology and chemistry.- Knowledge of computational biology and demonstrated experience in incorporating it into machine learning models.- Understanding of the AI/ML lifecycle, including data handling, feature engineering, model training, and optimisation
