18 machine-learning-phd positions at Radboud University

PhD Candidates for Social Studies of Scholarly Communication and Peer Review

for Science in Society (ISiS) as one of two PhD candidates! The first PhD project mainly involves a quantitative social science study of how the transformative developments affect the content of peer review

PhD Candidate: Visual Perception and Decision-Making

, and decision confidence. Research methods include computational modelling, brain imaging (fMRI, MEG), machine learning, behavioural methods, and other techniques. Virtually every decision we make comes

PhD Candidate: Computational Cognitive Science at the Donders Centre for Cognition

deeply and critically? If so, this position may be for you. In this PhD project, titled ’Cognitive capacities and machines: Why do calculators exist, if AGI cannot?’, you will adopt various meta

PhD Candidate: Neuromorphic Computing for Science

you interested in a project at the interface between physics, machine learning and neuromorphic computing? Do you thrive for both fundamental and societal impact of your research? We have a project for

Assistant Professor Big Data Astronomy

learning techniques are increasingly required. The Radboud astrophysics group has extensive expertise, for example in star and stellar-population models; machine-learning techniques for the extraction

PhD Candidate: Enhancing Marketing Interactions Using Virtual Humans

communication for improving well-being. A key role is played by techniques and tools from the area of Artificial Intelligence: innovative methods such as machine learning and knowledge representation are used

Lecturer in Critical Intersectional Perspectives on AI

, machine learning, robotics and symbolic AI, Bachelor’s students are also trained in courses such as Theoretical Modelling for Cognitive Science, AI as a Science, or Human-Computer Interaction. In our

PhD Candidate in Human-Centered Generative AI for Software Engineering

this multidisciplinary PhD project supervised by Dr Mairieli Wessel and Prof. Frits Vaandrager, you will combine insights from software engineering, human-computer interaction and machine learning by applying empirical

Assistant Professor Big Data Astronomy

and the LOFAR radio telescope as well as SKA in the future. To fully understand this data, complex numerical simulations and machine learning techniques are increasingly required. The Radboud

PhD Candidate: Active Learning of Peptide-based Materials

of supramolecular, peptide-based soft materials and connect this to machine learning to navigate the peptide sequence space, and to optimise the properties of promising candidates. In this project, you will use state