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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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. The scope of the research will encompass aspects such as network monitoring, routing algorithms and network-hardware benchmarking. About you You should possess a MSc/MEng in Engineering, Computer
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on transplant using multimodal medical data. You will be responsible for literature review, data cleaning, model development and implementation. You should possess a relevant PhD (or near completion) in
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research aims to develop new AI for shared human-AI decision-making in healthcare imaging. This post is focused on AI-assisted ultrasound guidance building on the group’s prior work on video and multi
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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
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, operations research, and management science, developing rigorous and practical methods for algorithmic risk assessment. You will become part of the broader AI2 collaboration, involving leading researchers from
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developing mathematical algorithms and simulations in MATLAB, in particular with Semidefinite Programming and Sum of Squares and of the analysis and design of feedback control systems using these approaches
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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team. You will lead in the design and implementation of statistical and computational algorithms of different datasets, and implement novel algorithms within the framework of existing code, providing
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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