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Field
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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foundation models, multi-modal learning algorithms, generative models, and large language models [1], have made seen remarkable advancements in the field of healthcare. It can lead to more accurate diagnosis
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in simulated environments and with real data on real UAVs. Defining and calculating measures for levels of trust in the developed algorithms is essential. These uncertainty-aware algorithms can self
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Deadline: 30 June 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the
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to make one appointment in the area of applied neuromorphic algorithm design. You will join the International Centre for Neuromorphic Systems - and be part of the wider pan-university community
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, and maintaining software systems to support these research projects. This includes building data pipelines, developing algorithms, and ensuring that data is stored efficiently and is accessible to all
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discipline boundaries to apply your work. We are seeking to make one appointment in the area of applied neuromorphic algorithm design. You will join the International Centre for Neuromorphic Systems - and be
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, programming languages, data structures and algorithms, operating systems, network security, visualization, and human-computer interaction, as well as participate in the full range of faculty responsibilities
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. You will work with experts in multiplex imaging and digital pathology, gaining experience with tools such as QuPath and both published and in-house image analysis algorithms. In this role, you will