This postdoctoral fellowship offers a unique opportunity to advance the use of artificial intelligence and machine learning for chemical synthesis and reactivity prediction in drug discovery. The successful candidate will develop and apply state‑of‑the‑art predictive models using large‑scale reaction datasets to improve chemical decision‑making, reaction optimisation, and molecular design, while collaborating with experts across data science, computational chemistry, medicinal chemistry, and synthesis technology. The project aims to accelerate the Design–Make–Test–Analyse cycle and contribute to next‑generation AI‑powered approaches for discovering new medicines. Location:
Basel, Switzerland Duration:
3 years Program start date:
October 1, 2026 Key Responsibilities
Analyze large‑scale chemical reaction datasets from proprietary and public sources to identify trends, opportunities, and challenges in chemical synthesis. Develop, implement, and evaluate machine learning models for predicting reaction success, reaction conditions, yield, regioselectivity, and molecular reactivity. Benchmark state‑of‑the‑art AI approaches, including graph neural networks, transformer models, and foundation models, against relevant synthesis prediction tasks. Investigate novel pre‑training strategies leveraging large‑scale chemistry and physics‑based datasets to improve predictive performance and generalisation. Collaborate closely with medicinal chemists, synthetic chemists, and automation experts to address real‑world drug discovery challenges. Apply predictive models to enable broader substrate scope exploration, reaction optimisation, and library synthesis design. Explore integration of synthesis prediction models with generative chemistry and AI‑driven molecular design workflows. Present research findings internally and externally, publish in leading scientific journals, and contribute to the broader scientific community. Essential Requirements
PhD in Data Science, Computer Science, Machine Learning, Cheminformatics, Computational Chemistry, Chemistry, Pharmaceutical Sciences, or a related quantitative discipline completed prior to the fellowship start date. Demonstrated record of scientific achievement (publications, presentations, patents, or equivalent). Demonstrated experience developing and applying deep learning methods to scientific or chemical datasets. Strong programming skills in Python and familiarity with modern machine learning frameworks and architectures, such as graph neural networks, transformers, large language models or foundation models for scientific applications. Experience with data analysis, statistical modelling, and handling large, complex datasets. Strong commitment to learning, innovation, and professional development. Ability to work effectively in highly collaborative, multidisciplinary research environments. Excellent communication skills and ability to present complex scientific concepts to diverse audiences. Eligible to work in Switzerland. Desirable Requirements
Experience curating, processing, and analysing large‑scale chemical reaction datasets, including reaction encoding, atom mapping, reaction classification, and extraction of chemical knowledge from structured or unstructured data sources. Experience designing and querying relational databases or other structured data systems for scientific data management and large‑scale analytics. Benefits
Guidance from accomplished scientific leaders and subject‑matter experts. Access to advanced technologies, platforms, and research capabilities. Collaboration across disciplines and organisational boundaries. A global and diverse community of postdoctoral fellows. Dedicated programming designed to help fellows thrive throughout their careers. Personalised experiential learning opportunities through a Postdoc Practicum that empower fellows to explore new scientific domains, build cross‑functional expertise, and expand their impact beyond their primary research project. Opportunities to present research, publish in leading journals, and build an international scientific network.
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Basel, Switzerland Duration:
3 years Program start date:
October 1, 2026 Key Responsibilities
Analyze large‑scale chemical reaction datasets from proprietary and public sources to identify trends, opportunities, and challenges in chemical synthesis. Develop, implement, and evaluate machine learning models for predicting reaction success, reaction conditions, yield, regioselectivity, and molecular reactivity. Benchmark state‑of‑the‑art AI approaches, including graph neural networks, transformer models, and foundation models, against relevant synthesis prediction tasks. Investigate novel pre‑training strategies leveraging large‑scale chemistry and physics‑based datasets to improve predictive performance and generalisation. Collaborate closely with medicinal chemists, synthetic chemists, and automation experts to address real‑world drug discovery challenges. Apply predictive models to enable broader substrate scope exploration, reaction optimisation, and library synthesis design. Explore integration of synthesis prediction models with generative chemistry and AI‑driven molecular design workflows. Present research findings internally and externally, publish in leading scientific journals, and contribute to the broader scientific community. Essential Requirements
PhD in Data Science, Computer Science, Machine Learning, Cheminformatics, Computational Chemistry, Chemistry, Pharmaceutical Sciences, or a related quantitative discipline completed prior to the fellowship start date. Demonstrated record of scientific achievement (publications, presentations, patents, or equivalent). Demonstrated experience developing and applying deep learning methods to scientific or chemical datasets. Strong programming skills in Python and familiarity with modern machine learning frameworks and architectures, such as graph neural networks, transformers, large language models or foundation models for scientific applications. Experience with data analysis, statistical modelling, and handling large, complex datasets. Strong commitment to learning, innovation, and professional development. Ability to work effectively in highly collaborative, multidisciplinary research environments. Excellent communication skills and ability to present complex scientific concepts to diverse audiences. Eligible to work in Switzerland. Desirable Requirements
Experience curating, processing, and analysing large‑scale chemical reaction datasets, including reaction encoding, atom mapping, reaction classification, and extraction of chemical knowledge from structured or unstructured data sources. Experience designing and querying relational databases or other structured data systems for scientific data management and large‑scale analytics. Benefits
Guidance from accomplished scientific leaders and subject‑matter experts. Access to advanced technologies, platforms, and research capabilities. Collaboration across disciplines and organisational boundaries. A global and diverse community of postdoctoral fellows. Dedicated programming designed to help fellows thrive throughout their careers. Personalised experiential learning opportunities through a Postdoc Practicum that empower fellows to explore new scientific domains, build cross‑functional expertise, and expand their impact beyond their primary research project. Opportunities to present research, publish in leading journals, and build an international scientific network.
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Data Science & AI Innovation Postdoctoral Fellow in Machine Learning for Chemical Synthesis and[...] Arbeitgeber: Novartis Farmacéutica
Novartis Farmacéutica ist ein hervorragender Arbeitgeber, der seinen Mitarbeitern in Basel, Schweiz, eine dynamische und unterstützende Arbeitsumgebung bietet. Mit einem starken Fokus auf Innovation und Zusammenarbeit fördert das Unternehmen die persönliche und berufliche Weiterentwicklung seiner Mitarbeiter durch umfassende Schulungsprogramme und spannende Projekte im Bereich Life Sciences und KI. Die attraktiven Vergütungen und umfangreichen Sozialleistungen machen Novartis zu einem idealen Ort für Fachkräfte, die einen bedeutenden Beitrag zur Gesundheitsversorgung leisten möchten.
