Associate Principal AI Research Scientist (Fundamental AI Research for Digital Biology)

AstraZeneca

20h•Hybrid

About The Position

Accountabilities Work as part of a high‑performing team to lead and deliver research projects, researching, developing and using novel AI theories, methodologies and algorithms with engineering best practices for a range of biology, chemistry and clinical applications. Lead and contribute to multifunctional projects 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 that support the discovery, design and optimisation of medicines with improved biological activity. Address fundamental AI research challenges and opportunities across the drug discovery and development value chain, providing innovative solutions in areas such as deep learning, representation learning, reinforcement learning, meta‑learning, active learning, search and optimisation, applied to domains including de novo molecule design, protein engineering, in‑silico discovery, structural biology, genetic engineering, synthetic biology, computational biology, translational sciences, biomarker discovery, clinical research and clinical trials. Design and develop machine learning models for heterogeneous biological data, collaborating with experimental scientists (e.g. in chemistry, discovery science and other experimental fields) to plan and interpret algorithmically designed wet‑lab experiments and inform future experimental directions. Translate complex scientific requirements into AI research problems and solution strategies, exploring different approaches and reasoning about trade‑offs to tackle diverse, complex challenges across multiple projects. Stay at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring and personal development initiatives, and by contributing to publications and academic/industry collaborations.

Requirements

PhD in machine learning, statistics, computer science, mathematics, physics or a related technical discipline, with relevant fundamental research experience in AI/ML, or equivalent practical experience.
Fundamental AI research experience with a strong 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.
Deep theoretical understanding and strong quantitative knowledge of algebra, algorithms, probability, calculus and statistics, combined with extensive hands-on experience in experimentation, analysis and visualisation of AI/ML techniques.
Well‑rounded experience designing new AI/ML approaches to derive insights from proprietary and external datasets and to generate testable hypotheses, using algorithmic, mathematical, computational and statistical methods combined with theoretical, empirical or experimental research approaches.
Experience in theoretical, fundamental AI research 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 rigorous scientific methodology to: Identify and create novel ML techniques and the data required to train models Develop machine learning model architectures and training algorithms Analyse and tune experimental results to inform future experimental directions Implement and scale training and inference frameworks Validate hypotheses in a reproducible manner
Distinctive experience in using anything from simple baseline tricks to cutting‑edge research methods to advance AI/ML capabilities, and in implementing them in an elegant, stable and scalable way.
Strong algorithmic development and programming experience in Python or similar languages, and standard machine learning toolkits, especially deep learning frameworks such as PyTorch, TensorFlow or similar.
Robust ability to communicate and collaborate effectively with diverse stakeholders, clearly presenting research findings and developments to scientists, engineers and domain experts from different disciplines, including non-AI audiences.
Fundamental research expertise and hands-on experience, combined with theoretical knowledge, in at least 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-parametrics Natural language processing, approximate inference, control theory, meta-learning, category theory Statistical mechanics, information theory, knowledge representation, supervised/unsupervised/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 Multimodality, computational linguistics, representation learning, foundations of generative modelling, computational geometry and geometric methods, multi-modal deep learning, information retrieval and related areas.

Nice To Haves

Fluency in Python, R and/or Julia or 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 a wide range of machine learning problems, particularly in biology, chemistry and clinical applications, with a demonstrated track record of solving biological problems 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), e.g. NeurIPS, ICML, ICLR, JMLR or similar.
A track record of successful collaboration with AI engineering teams to deliver complex machine learning models and production‑ready data and analytics products.
Practical experience working in cloud computing environments such as AWS, GCP or 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/tissue biology.
Evidence of open‑source projects, patents, personal portfolios, products, peer-reviewed publications or similar achievements.

Responsibilities

Work as part of a high‑performing team to lead and deliver research projects, researching, developing and using novel AI theories, methodologies and algorithms with engineering best practices for a range of biology, chemistry and clinical applications.
Lead and contribute to multifunctional projects 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 that support the discovery, design and optimisation of medicines with improved biological activity.
Address fundamental AI research challenges and opportunities across the drug discovery and development value chain, providing innovative solutions in areas such as deep learning, representation learning, reinforcement learning, meta‑learning, active learning, search and optimisation, applied to domains including de novo molecule design, protein engineering, in‑silico discovery, structural biology, genetic engineering, synthetic biology, computational biology, translational sciences, biomarker discovery, clinical research and clinical trials.
Design and develop machine learning models for heterogeneous biological data, collaborating with experimental scientists (e.g. in chemistry, discovery science and other experimental fields) to plan and interpret algorithmically designed wet‑lab experiments and inform future experimental directions.
Translate complex scientific requirements into AI research problems and solution strategies, exploring different approaches and reasoning about trade‑offs to tackle diverse, complex challenges across multiple projects.
Stay at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring and personal development initiatives, and by contributing to publications and academic/industry collaborations.

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