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

Accountabilities:

You will work efficiently in a team tolead anddeliver projects optimallyresearchingdevelopingand using the novel AI theories methodologies and algorithms with engineering best practices and standard processes for various biologychemistryand clinicalapplications.

You will be partandalsoleadmultifunctionalprojectsto 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 willlead andcontribute toaddressing 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 biologygenetic engineering synthetic biologycomputational biology translational sciences biomarker discovery clinical research clinical trials and many other areas.

You willlead anddevelop machine learning models designed explicitly foranalysingheterogeneous 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 byparticipatingin 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 relevantfundamentalresearch experience in artificial intelligence and machine learningor equivalent practicalexperience.

Fundamental AI research experience in conjunction with foundational knowledge and a proventrack recordin conceptualising designing and creating entirely new models methods approaches architectures and algorithms from scratch. This is essential as off-the-shelf methods andstate-of-the-artAI/ML techniques often do not work on our scientific problems and datasets.

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

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 theoreticalempiricalor experimental research sciences 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 applying rigorous scientific methodology to (i) identify and create novel ML techniques and the required data to trainmodels (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 inexploitingthe simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant stable and scalable way.

Thoroughalgorithmic developmentand programmingexperience 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 scientistsengineersand domain experts from different disciplines.

Fundamental researchextensive research andexpertunderstanding combined with hands-on practical experience and theoretical knowledge ofat leasttwo or moreof 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 demonstratedtrack recordfor solving biological issues relevant to drug discovery and development.

Research experiencedemonstratedby journal and conference publications in prestigious venues (with at least one publication as a leading author). Examples include but are not limited toNeurIPS ICML ICLR and JMLR.

A track recordof 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 -person work gives us the platform we need to connect work at pace and challengeperceptions.Thatswhy we work on average a minimum of three days per week from the office. But thatdoesntmeanwerenot 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 preventingmodifying and even curing some of the worlds most complex diseases. Here we have the potential to grow our pipeline and positivelyimpactthe 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!