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The focus area for this role is to provide QSAR, cheminformatics and machine learned modelling support to Crop Protection Research & Development. Working alongside multidisciplinary project teams, your primary responsibility is to evaluate and build predictive models relating chemical structure and/or properties to endpoints relevant to agrochemical research (e.g., ADME, (eco)toxicology, soil properties, biological efficacy, crop selectivity). With these models, you will help teams to identify high quality compounds with the right balance of properties and inform chemistry design and testing strategies. Building on your passion for science and innovation, you will join an active network of QSAR experts and specialist modellers at Syngenta to champion the use of predictive models and digital thinking.
You will have a PhD or equivalent experience in cheminformatics or a related field and a good knowledge of state-of-the-art approaches to QSAR modelling and their utility. Proficiency in coding with Python and/or R and familiarity with standard Machine Learning and cheminformatics packages is expected. Experience with QSAR in the context of drug discovery is an advantage, as are practical skills in data preparation and model deployment. Excellent communication skills and the ability to work collaboratively in a multi-disciplinary team are essential.