Fundamental AI Research Scientist - Toronto, Ontario

Are you passionate about fundamental artificial intelligence research to address real-world science applications? Does building novel AI solutions from first principles that contribute to preventing, modifying, and even curing some of the world's most complex diseases inspire you? 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.

We are looking for people with both hands-on practical experience and deep theoretical knowledge in fundamental research areas such reasoning, causal inference, deep learning, reinforcement learning, world models, non-convex optimisation, statistical inference, probability theory, computational geometry, multi-task learning, representation learning, multi-scale modelling, multi-property optimization, natural language processing, control theory, meta-learning, category theory, complex systems, statistical mechanics, information theory, knowledge representation, search and optimisation, transfer learning, probabilistic programming, computational linguistics,, and geometric methods.

Join our interdisciplinary Centre for Artificial Intelligence team working on the frontier of AI research for science. Your work will support the next generation of medicines and vaccines at the intersection of AI, biology, and engineering. Your work will help transform the drug discovery and development value chain as we know it, uncovering novel biological insights, automating processes, streamlining decision-making, and improving the overall pipeline across all therapeutic areas at AstraZeneca.

Accountabilities:

•You will work efficiently in a team to work on fundamental AI research problems, customise solutions to various applications and 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, genetic engineering, synthetic 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 equivalent practical experience.

•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 AI/ML 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 (v) validate hypotheses.

•Theoretical understanding, combined with a strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, hands-on experimentation, analysis, and AI/ML techniques visualisation.

•Algorithmic development and programming experience in Python or other programming languages and machine learning toolkits, especially deep learning (e.g., Pytorch, TensorFlow, etc.).

•Ability to communicate and collaborate effectively with diverse individuals and functions, reporting and presenting research findings and developments clearly and efficiently to other scientists, engineers and domain experts from different disciplines.

•Fundamental research with hands-on practical experience an