48 big-data-and-machine-learning-phd Fellowship positions at University of Birmingham in United-States
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postdoctoral Research Fellow (RF) position for one year with a possible extension for one more year. The starting date is November or December 2025. This post will advance the application of Machine Learning (ML
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can be leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models
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Strong analytical skills and experience in developing and implementing machine learning/AI solutions using relevant languages and frameworks Excellent communication skills and proven ability to collaborate
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analysing large scale bacterial evolution experiments, including extensive sequencing analysis Contribute to designing, performing and analysing in vitro pathogenicity assays Work within the specified
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reasoning, machine learning, or equivalent qualifications Proven ability to publish in top-tier conferences and journals in AI, computer-aided verification, automated reasoning, or quantum computing
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department, recently ranked top in the UK for 4-star-category research by the Research Excellence Framework 2021. It has over 130 academic and research staff together with 120 PhD students, and a wide-ranging
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Specification PhD (or near to completion) in high energy particle physics Experience with operation and use of Roman pot-based near-beam spectrometers Experience with analysis of data from Roman pot-based near
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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comprise both the development of bioinformatics pipelines and the application of novel machine learning methods for interpreting microbiome and host ‘omics data from faecal, intestinal biopsy and saliva
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holder will be responsible for supporting laboratory running and research project delivery in the area of polymer science, including characterising data collection interpretation. Training will be provided