Senior AI Scientist

Senior AI Scientist (Fundamental AI Research for Science) Location Barcelona (Spain)

Introduction to the opportunity:

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 worlds 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 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 and with relevant fundamental research experience in artificial intelligence and machine learning OR 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 experience and well-rounded hands-on ability to understand and 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 tune experimental results to inform future experimental directions and (iv) implement and scale training and inference engineering frameworks and (v) validate hypotheses.

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.

Deep theoretical understanding combined with a strong quantitative knowledge of algebra algorithms probability calculus and statistics as well as hands-on experimentation analysis and AI/ML techniques visualisation.

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

Experience in 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.

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 hands-on practical experience and theoretical knowledge of at least one 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:

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 often do not work on our scientific problems and datasets.

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. Thats why we work on average a minimum of three days per week from the office. But that doesnt mean were 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 worlds 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!