52 algorithm-development-"Multiple" "NTNU Norwegian University of Science and Technology" Fellowship positions at University of Birmingham
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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-leading database of MRI images of childhood tumours and have developed AI approaches to diagnose different types of tumour. To be useful for patients, this needs to be delivered in hospitals in real time
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing
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project by: Collecting, analysing and critically interpretating data sets from multiple techniques to characterise granular zeolite ion-exchange materials. Designing and developing new in situ synchrotron
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holder will play a key role in supporting quantitative data analysis, including the development of statistical analysis plans, and interpretation in a cluster trial setting. There are also opportunities
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inversion techniques and signal processing. Strong programming skills, Proficiency in scientific computing (e.g. Python, MATLAB, or similar) for algorithm development and data handling. Experience with sensor
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Experience with machine learning algorithms and ideally experience developing novel methods Understanding of basic biological principles and experience interpreting ‘omics data Ability to analyse information
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, with opportunities to build an international research portfolio. The post holder will work on developing and testing study design and data analysis methods, particularly related to cluster randomised
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partners. Main Duties Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species
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-learningdisability/ ) a team of researchers showed that adults with Profound and Multiple Learning Disabilities (PMLD) and their family carers were disproportionately impacted by the COVID-19 pandemic. The post holder