PhD Position: Autonomous Discovery and Control of Chemical Reaction Systems

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.

PhD Position: Autonomous Discovery and Control of Chemical Reaction Systems

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.

PhD Position: Autonomous Discovery and Control of Chemical Reaction Systems

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.

Historical Data Specialist for research infrastructure position: Chains of the Past

‘Chains of the Past’ sits at the frontier of digital historical research. By processing thousands of historical handwritten documents, this project builds a data infrastructure that will support future research into the lives of enslaved and free people in the former Dutch colonies. As a data specialist, you will help lay the foundations for this new and innovative research landscape. Applying already existing technical data extraction methods to colonial archives requires creativity and critical thinking.

Historical sources are fragmented, shaped by colonial power relations, and often difficult to interpret computationally. Your role is to bridge the worlds of programming and historical interpretation: developing robust entity recognition methods while remaining attentive to the historical context that gives meaning to the data. We therefore seek someone who is eager to learn and likes working in interdisciplinary teams and settings. If desired, we are happy to support you to develop your own research ideas.

Using and further developing existing data extraction models in R and/or Python, you will help connect individual observations from different sources, enabling the reconstruction of life histories of both enslaved and free people in the colonial context. Together with key stakeholders such as the Dutch Digital Heritage Network (NDE) and the National Archives of the Netherlands, Suriname and Curaçao, you will also develop a strategy to generate sustainable and persistent identifiers (PIDs) for historical person reconstructions. These identifiers will make it possible to easily follow people across multiple archival sources and colonial contexts. To support this work, you will develop and publish Linked Open Data (LOD) that ensures the data can be reused and connected within the wider digital heritage ecosystem.

Historical Data Specialist for research infrastructure position: Chains of the Past

‘Chains of the Past’ sits at the frontier of digital historical research. By processing thousands of historical handwritten documents, this project builds a data infrastructure that will support future research into the lives of enslaved and free people in the former Dutch colonies. As a data specialist, you will help lay the foundations for this new and innovative research landscape. Applying already existing technical data extraction methods to colonial archives requires creativity and critical thinking.

Historical sources are fragmented, shaped by colonial power relations, and often difficult to interpret computationally. Your role is to bridge the worlds of programming and historical interpretation: developing robust entity recognition methods while remaining attentive to the historical context that gives meaning to the data. We therefore seek someone who is eager to learn and likes working in interdisciplinary teams and settings. If desired, we are happy to support you to develop your own research ideas.

Using and further developing existing data extraction models in R and/or Python, you will help connect individual observations from different sources, enabling the reconstruction of life histories of both enslaved and free people in the colonial context. Together with key stakeholders such as the Dutch Digital Heritage Network (NDE) and the National Archives of the Netherlands, Suriname and Curaçao, you will also develop a strategy to generate sustainable and persistent identifiers (PIDs) for historical person reconstructions. These identifiers will make it possible to easily follow people across multiple archival sources and colonial contexts. To support this work, you will develop and publish Linked Open Data (LOD) that ensures the data can be reused and connected within the wider digital heritage ecosystem.