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20th October 2026 Languages English English English The Department of Biology has a vacancy for a PhD Candidate in Modelling Marine Larval Dispersal and Spatial Population Genetics and Demography
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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workshops. Project description Many proposed quantum algorithms for optimization, including the quantum approximate optimization algorithm (QAOA) and variational quantum-circuit approaches are formulated and
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are also expected to present research findings at international conferences and workshops. Project description Many proposed quantum algorithms for optimization, including the quantum approximate
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and implement a new version of the group's pathogen detection software, Voyager. Contribute to algorithm development for sequence analysis and related computational problems Collaborate closely with
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further. Another direction of the project will be to investigate the interplay between standard digital implementations of the approximate quantum optimization algorithm (QAOA) and analogous continuous time
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the interplay between standard digital implementations of the approximate quantum optimization algorithm (QAOA) and analogous continuous time implementations. The project will make use of classical simulations
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, such as decision rights, transparency, human override, and visible oversight, shape perceptions of legitimacy. A particular focus is the boundary between AI-augmented leadership and de facto algorithmic
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, but for example: University of Ulm (Germany): Algorithms for wearable data analysis University of Manchester (UK): Mathematical modeling of hormone rhythms Qualifications and personal qualities: We
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algorithms and techniques at the MAC layer for fast anomaly detection, failure prediction, autonomous recovery, and performance resilience. Service-aware MAC layer techniques – develop novel MAC-layer