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Field
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encouraged to adopt a creative approach to problem-solving, exploring various deep learning techniques. Verification of these models and algorithms will be conducted using benchmark datasets and real-world
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algorithms, have excelled in tasks like computer vision, image recognition and large language models (LLM). However, their reliance on extensive computational resources results in excessively high energy
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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thesis shall highlight the proposed risk embedding technique into MASs path planning algorithms, enabling them to realise guaranteed, conservative, yet risk-feasible trajectories for efficient state-space
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maintenance, production efficiency, and quality control. While the benefits of ML are significant, its adoption also introduces risks such as data privacy concerns, algorithmic bias, model transparency issues
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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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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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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