Senior AI Research Scientist (Fundamental AI Research for Digital Biology)

This role is based in Barcelona, with an on-site commitment of three days a week. Fluency in English is required. 

Introduction to the opportunity: 

Are you passionate about creating artificial intelligence and machine learning systems 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 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 working on the frontier of AI research for digital biology. Your work will support the next generation of medicines and vaccines at the intersection of AI, biology, and engineering. Your work will contribute to transforming the drug discovery and development value chain as we know it, uncovering novel biological insights, automating processes, streamlining decisions, and improving the overall pipeline across all therapeutic areas at AstraZeneca. 

Accountabilities:     

•You will work efficiently in a team to deliver projects optimally, researching, developing and using the novel AI theories, methodologies, and algorithms, with engineering best practices and standard processes for various biology, chemistry and clinical applications. 

•You will be part of multifunctional teams to conceive, design, develop and conduct experiments to test hypotheses, validate new approaches, and compare the effectiveness of different AI/ML systems, algorithms, methods and tools for new applications to support the discovery, design, and optimisation of medicines with improved biological activity. 

•You will contribute to addressing 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 molecule design, protein engineering, in-silico discovery, structural biology, computational biology, translational sciences, biomarker discovery, clinical research, clinical trials and many other areas.     

•You will develop machine learning models designed explicitly for analysing heterogeneous biological data while collaborating with biology researchers to run algorithmically designed wet lab experiments to inform future experimental directions.   

•You will remain at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring, and personal development initiatives and contributing to publications and academic and industry collaborations. 

Essential Skills/Experience:     

•A PhD in machine learning, statistics, computer science, mathematics, physics, or a related technical discipline, with relevant fundamental research experience in artificial intelligence and machine learning OR an MSc with a few years of relevant experience in the research and development of artificial intelligence and machine learning approaches to life sciences applications. 

•Fundamental AI research and development experience with well-rounded hands-on ability to implement AI/ML techniques based on publications or developed entirely in-house. In addition, experience in applying rigorous scientific methodology to (i) identify and create ML techniques and the required data to train models, (ii) develop machine learning model architectures and training algorithms, (iii) analyse and fine tune experimental results to inform future experimental directions, and (iv) implement and scale training and inference engineering frameworks and (