We have a variety of positions at different levels.Accountabilities: You will work efficiently in a team to deliver projects optimally using the latest AI/ML methods, approaches, and techniques, with engineering best practices and standard processes. You will be part of multifunctional teams, particularly with our partners from Biologics Engineering, to develop machine learning methods and tools for discovering, designing, and optimising large molecules such as antibodies with improved biological activity. You will analyse 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, and active learning approaches applied to de novo protein design, protein engineering, in-silico discovery, structural biology, computational biology, and many other areas. You will remain at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring, personal development initiatives, and contributing to publications and academic and industry collaborations.Essential Skills/Experience: A PhD in computer science, statistics, mathematics, physics, biology or related field OR MSc and proven experience developing artificial intelligence and machine learning models.Knowledge of computational biology and demonstrated experience in incorporating it into machine learning models.Understanding of the AI/ML lifecycle, including data handling, feature engineering, model training, and optimisation.Ability to exploit the simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant, stable, and scalable way.Desirable Skills/Experience: 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.Demonstrated experience incorporating them into artificial intelligence approaches and machine learning models.Fluent in Python, R, and/or Julia and other programming languages, including scientific packages and libraries (e.G. PyTorch, TensorFlow, Pandas, Numpy, Matplotlib).Practical ability to work on cloud computing environments like AWS, GCP, and Azure.In-depth knowledge of one or more of the following areas such as causal inference, machine learning, deep learning, control theory, natural language processing, multi-scale modelling, reinforcement learning, mathematical optimisation and simulation, signal processing, game theory, statistical inference, operations research, pattern recognition, recommendation systems, probabilistic programming and/or related areas.#J-18808-Ljbffr
