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Field
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relevant to integration of machine learning and mechanistic models, and development of engines for efficient processing and visualisation of large-scale datasets and system geographic information and
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning
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selection. This research is conducted in a collaborative environment within the lab and larger unit, so the participant will learn how to perform both independently in the field and as part of larger teams
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projects related to comparative effectiveness and patient-centered outcomes research, all in support of Office of the Secretary priorities led by ASPE. Learning Objectives: Under the guidance of a mentor
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of 17% superannuation applies. Research Fellow in machine-learning enabled digital forensics Fixed term, full-time 36-month position available About the position We are seeking a Research Fellow with
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record
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Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm
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of the Armed Forces Covenant and welcome applications from service people. Further information For further information please contact Susannah Crockford, e-mail s.crockford@exeter.ac.uk . The closing date
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with verifiable experience in longitudinal data analyses methods, structural equation modelling, network analysis, machine learning and/or adaptive trial design. Essential are outstanding academic
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: Research activities will be conducted in BSL-2+ laboratories. Learning Objectives: Under the guidance of a mentor, the participant will gain experience in: Animal studies: mouse and large animal inoculation