Postdoctoral Researcher

retrosynthesis and reaction prediction. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex molecules in chemistry, pharmaceuticals, and agrochemicals. The position provides a unique opportunity to work at the forefront of computational chemistry and AI, contributing to high-impact research with real-world applications in green chemistry and industrial synthesis. Key Responsibilities Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes. Curate and manage reaction datasets from literature, patents, and proprietary sources to train and validate predictive models. Integrate retrosynthesis tools with cheminformatics platforms and molecular modeling software. Collaborate with synthetic chemists to experimentally validate predicted retrosynthetic routes and optimize laboratory workflows. Contribute to scholarly publications in high-impact journals and represent the research group in conferences and workshops. Mentor graduate students and participate in capacity-building activities within the research team. Required Qualifications Ph.D. in Chemistry, Computational Chemistry, Cheminformatics, Computer Science, or a related discipline. Proven expertise in machine learning/deep learning applied to chemical reaction prediction or retrosynthesis (e.g., reaction templates, template-free approaches). Proficiency in Python programming and familiarity with ML frameworks such as TensorFlow, PyTorch, or JAX. Experience with cheminformatics tools (e.g., RDKit, Open Babel) and chemical reaction databases (e.g., Reaxys, USPTO, Pistachio). Understanding of organic synthesis and reaction mechanisms. Publication record demonstrating expertise in AI and computational chemistry. Excellent problem-solving skills and the ability to work collaboratively in a multidisciplinary research environment. Preferred Skills Experience in graph-based learning, attention mechanisms, and transformer architectures applied to chemical data. Knowledge of computer-aided synthesis planning (CASP) tools and retrosynthetic analysis software (e.g., AiZynthFinder, ASKCOS, IBM RXN). Familiarity with reaction condition prediction and reaction yield optimization. Exposure to quantum chemistry (DFT) and molecular simulations is a plus. Experience with cloud computing and/or high-performance computing (HPC) resources. Application Process Interested candidates should submit the following documents as a single PDF: Cover letter describing research interests, motivation, and relevant experience Detailed Curriculum Vitae (CV) with a list of publications Contact details for at least two academic referees