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Field
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Skills: Proficiency in Python, TensorFlow, PyTorch, or similar frameworks. MS or higher degree (PhD preferred) degree in Computational Neuroscience, Computer Science, Bioinformatics, or a related field
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and motivated PhD student to contribute to the development of the next generation of DNA computers, capable of processing large molecular databases. The project will combine concepts from molecular
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Trustworthy Graph Machine Learning for Population Scale Networks Job description We invite applications for a postdoctoral researcher to work on fundamental techniques for trustworthy graph machine
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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spectral analysis, entropy-based metrics, graph representations of cardiac conduction, and supervised, unsupervised, and deep learning approaches for classification of abnormal electrical activity within
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-identification standards and privacy-preserving data techniques relevant to biomedical research. Master’s or PhD in Computer Science, Data Science, Bioinformatics, Biomedical Informatics, or a related field
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The University of Exeter’s Department of Computer Science invites applications for a funded PhD studentship to commence on 21 September 2026, or as soon as possible thereafter. For eligible students
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reputable scientific journals and present at relevant conferences to contribute to the academic and professional discourse in their respective field. Supervising PhD Students: Collaborate with and provide
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project Can AI interpret graphs like a human materials scientist that relates diagrams to composition temperature, pressure, synthesis, processing and uncertainty? The PhD candidate will develop and
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Qualifications:. PhD in one of these fields: Computer Science, Data Science, Biomedical Informatics, Computational Biology, Information Science or a related field. Experience in at least one of the following