Jobid=620217017603862497 (0.0994)
This PhD position offers you the opportunity to work at the interface of systems chemistry, analytical science and machine‑learning‑guided experimentation. You will contribute to a research initiative aimed at mapping, understanding and controlling the behaviour in multicomponent chemical systems contributing both to advancing new synthetic processes and understanding prebiotic chemical complexity.
Your primary goal is to map the reactivity landscape of a diverse set of molecular building blocks. You will perform high‑throughput mixture experiments and characterise complex reaction outcomes using analytical methods such as NMR spectroscopy, LC‑MS, chromatography and automated data processing. These experiments will generate foundational datasets describing how molecular diversity and functional group variety shape emergent reactivity.
You will then design and construct minimal multicomponent reaction systems to study behaviours such as kinetic competition, autocatalytic or selective amplification processes and other emergent network‑level behaviours. You will investigate how these features arise from interacting subsystems and how they can be modulated or combined.
A central part of the PhD involves developing closed‑loop, machine‑learning‑guided workflows. In collaboration with computational partners, you will implement algorithms that design new experiments, optimise product distributions and autonomously steer chemical systems towards predetermined objectives. Teaching duties (approx. 10% of your working time) may include assisting in chemistry laboratory courses or supervising undergraduate research projects.
Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.
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