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are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities Develop machine learning methods and tools with a
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. The studentship will start on 1st October 2026. Project Description Project Aim To develop and validate an AI co-pilot software system integrating multi-modal radiomics data to enhance cancer detection speed and
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experimental research as well as data analysis and algorithm development. Students with either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in engineering, mathematics, neuroscience
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candidate will develop and conduct empirical research examining human interaction with algorithmic decision-support systems in clinical contexts. The precise focus of the project will be refined
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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preferences into AI system design and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute
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will develop and apply computational methods for the analysis of cell-free DNA (cfDNA) sequencing data, supporting a growing research program at the intersection of epigenomics and translational medicine
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PhD Doctoral Candidate to carry out research in neuromorphic photonic-electronic integrated circuits for brain-inspired information processing and sensing and in the development of efficient learning
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with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from