AstraZeneca SAS
Senior AI Research Scientist (Fundamental AI Research for Digital Biology)
AstraZeneca SAS, West Palm Beach, Florida, United States, 33412
Role location and language
This role is based in Barcelona, with an on-site commitment of three days a week. Fluency in English is required.
Overview
Introduction to the opportunity: Are you passionate about creating artificial intelligence and machine learning systems for real-world science applications? Does contributing to preventing, modifying, and curing some of the world\'s most complex diseases inspire you? Would you like to work on 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 frontier of AI research for digital biology. Your work will support 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, researching, developing and using novel AI theories, methodologies, and algorithms, with engineering best practices and standard processes for various biology, chemistry and clinical applications. 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 systems, algorithms, methods and tools for new applications to support the discovery, design, and optimisation of medicines 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, with relevant fundamental research experience in artificial intelligence and machine learning OR an 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 and development experience with hands-on ability to implement AI/ML techniques based on publications or developed entirely in-house. Experience in applying rigorous scientific methodology to identify and create ML techniques and the required data to train models, develop model architectures and training algorithms, analyse and fine-tune experimental results to inform future directions, and implement and scale training and inference engineering frameworks and validate hypotheses. Theoretical understanding combined with strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, with hands-on experimentation and AI/ML technique visualization. Algorithmic development and programming experience in Python or other languages and ML toolkits, especially deep learning (e.g., PyTorch, TensorFlow). Experience in practical aspects of AI/ML foundations and model design, such as improving experimentation and analysis of 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 to scientists, engineers and domain experts. Fundamental research with hands-on practical experience and theoretical knowledge of at least one of the following areas: multi-agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametric methods, NLP, approximate inference, control theory, meta-learning, pattern recognition, large language models, probabilistic programming, and related fields. Desirable Skills/Experience
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 approaches. Fluent in Python, R, and/or Julia, and experience with scientific packages and libraries (e.g., PyTorch, TensorFlow, Pandas, NumPy, Matplotlib). Research experience demonstrated by journal and conference publications in prestigious venues (with at least one leading author publication). 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, bioinformatics, and 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. We balance the expectation of being in the office while respecting individual flexibility. 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. Note : This description is a refined version of the original posting for readability and compliance. We are committed to building an inclusive and diverse team representing all backgrounds and perspectives. We comply with applicable laws and regulations on non-discrimination in employment and recruitment, as well as work authorization and employment eligibility verification requirements.
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Introduction to the opportunity: Are you passionate about creating artificial intelligence and machine learning systems for real-world science applications? Does contributing to preventing, modifying, and curing some of the world\'s most complex diseases inspire you? Would you like to work on 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 frontier of AI research for digital biology. Your work will support 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, researching, developing and using novel AI theories, methodologies, and algorithms, with engineering best practices and standard processes for various biology, chemistry and clinical applications. 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 systems, algorithms, methods and tools for new applications to support the discovery, design, and optimisation of medicines 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, with relevant fundamental research experience in artificial intelligence and machine learning OR an 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 and development experience with hands-on ability to implement AI/ML techniques based on publications or developed entirely in-house. Experience in applying rigorous scientific methodology to identify and create ML techniques and the required data to train models, develop model architectures and training algorithms, analyse and fine-tune experimental results to inform future directions, and implement and scale training and inference engineering frameworks and validate hypotheses. Theoretical understanding combined with strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, with hands-on experimentation and AI/ML technique visualization. Algorithmic development and programming experience in Python or other languages and ML toolkits, especially deep learning (e.g., PyTorch, TensorFlow). Experience in practical aspects of AI/ML foundations and model design, such as improving experimentation and analysis of 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 to scientists, engineers and domain experts. Fundamental research with hands-on practical experience and theoretical knowledge of at least one of the following areas: multi-agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametric methods, NLP, approximate inference, control theory, meta-learning, pattern recognition, large language models, probabilistic programming, and related fields. Desirable Skills/Experience
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 approaches. Fluent in Python, R, and/or Julia, and experience with scientific packages and libraries (e.g., PyTorch, TensorFlow, Pandas, NumPy, Matplotlib). Research experience demonstrated by journal and conference publications in prestigious venues (with at least one leading author publication). 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, bioinformatics, and 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. We balance the expectation of being in the office while respecting individual flexibility. 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. Note : This description is a refined version of the original posting for readability and compliance. We are committed to building an inclusive and diverse team representing all backgrounds and perspectives. We comply with applicable laws and regulations on non-discrimination in employment and recruitment, as well as work authorization and employment eligibility verification requirements.
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