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

Accountabilities:     

•You will work efficiently in a team to lead 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 and also lead 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 to support the discovery, design, and optimisation of medicines with improved biological activity. 

•You will lead and 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 lead and 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 experience in conjunction with 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.  

•Deep theoretical understanding, combined with a strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, as well as extensive 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 theoretical, empirical or 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 train models, (ii) devel