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 working on 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, developing and using the latest AI/ML methods, approaches, and techniques, with engineering best practices and standard processes for various biology, chemistry and clinical problems.    

•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 algorithms, methods and tools for discovering, designing, and optimising molecules 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, biophysics, 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, biology, or a related technical discipline and relevant experience in research and development of artificial intelligence and machine learning based solutions OR MSc with few years of relevant experience in research and development of artificial intelligence and machine learning approaches to life sciences applications. 

•Extensive practical expertise in understanding and implementing AI/ML techniques based on publications or developed entirely in-house. In addition, foundational knowledge and a proven track record in conceptualising, designing, and creating entirely new models, methods, approaches, architectures, and algorithms from scratch. This is essential as off-the-shelf methods and state-of-the-art AI/ML techniques only sometimes work on our scientific problems and datasets. 

•Deep theoretical knowledge, extensive hands-on experimentation, analysis, and visualisation of AI/ML techniques, in conjunction with a strong understanding of linear algebra, calculus, and statistics. 

•Well-rounded experience designing new AI/ML approaches to deriving insights from proprietary and external datasets to generate testable hypotheses using algorithmic, mathematical, computational, and statistical methods combined with theoretical, empirical or experimental research sciences approaches. 

•Experience in theoretical and practical aspects of AI/ML foundations and model design, such as improving model efficiency, quantisation, conditional computation, reducing bias, or achieving explainability in complex models. 

•In-depth understanding of applying rigorous scientific methodology to (i) identify and create novel ML techniques and the required data to train models, (ii) develop machine learning model' architectures and training algorithms, (iii) analyse and tune experimental results to inform future experimental directions, and (iv) implement and scale training and inference engineering frameworks and (v) validate hypotheses. 

•Distinctive experience in exploiting the simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant, stable, and scalable way.  

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

•Robust 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. 

•Extensive research and in-depth understanding combined with hands-on practical experience and theoretical knowledge applied to real-world applications of two or more of the following research areas - examples include but are not limited to - multi-agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametric, natural language processing, approximate inference, control theory, meta-learning, category theory, statistical mechanics, information theory, knowledge representation, unsupervised, supervised, semi-supervised learning, computational complexity, search and optimisation, artificial neural networks, multi-scale modelling, transfer learning, mathematical optimisation and simulation, planning and control modelling, time series foundation models, federated learning, game theory, statistical inference, pattern recognition, large language models, probability theory, probabilistic programming, Bayesian statistics, applied mathematics, multimodality, computational linguistics, representation learning, foundations of generative modelling, computational geometry and geometric methods, multi-modal deep learning, information retrieval and/or related areas.       

Desirable Skills/Experience:    

•Fluent in Python, R, and/or Julia other programming languages, including scientific packages and libraries (e.g. PyTorch, TensorFlow, Pandas, NumPy, Matplotlib).  

•Experience in machine learning research and developing fundamental algorithms and frameworks that can be applied to various machine learning problems, particularly in biology, chemistry and clinical applications and a demonstrated track record for solving biological issues relevant to drug discovery and development.    

•Research experience demonstrated by journal and conference publications in prestigious venues (with at least one publication as a leading author). Examples include but are not limited to NeurIPS, ICML, ICLR and JMLR.     

•A track record of successfully collaborating with AI engineering teams to deliver complex machine learning models and production-ready data and analytics products.    

•Practical ability to work on cloud computing environments like AWS, GCP, and Azure.     

•Domain knowledge of tools, techniques, methods, software, and approaches in one or more areas, such as protein engineering, microbiology, structural biology, molecular design, biochemistry, genomics, genetics, bioinformatics, molecular, cellular and tissue biology.     

•Evidence of open-source projects, patents, personal portfolios, products, peer-reviewed publications, or similar track records.   

Why AstraZeneca?  

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person work gives us the platform we need to connect, work at pace, and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. Join the team, unlocking the power of what science can do. We are working towards treating, preventing, modifying, and even curing some of the world's most complex diseases. Here, we have the potential to grow our pipeline and positively impact the lives of billions of patients around the world. We are committed to making a difference. We have built our business around our passion for science. Now, we are fusing data and technology with the latest scientific innovations to achieve the next wave of breakthroughs.        

Ready to make a difference?    

Apply now and join us in our mission to push the boundaries of science and deliver life-changing medicines!   

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.