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(essential). Advanced experience in GIS (essential). Appropriate experience (essential) in either (1) machine learning (fully-convolutional neural networks) applied to remotely sensed data, either/or (2
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mass cytometry. A bioinformatician, conversant with different machine learning and modelling of complex datasets requiring integration. S/he will curate, clean and analyse all data, developing
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-informed machine learning and data driven approaches for dynamical process modelling and monitoring. Develop a computational intelligence approach to utilize sensor data with computational process models
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Language Processing (NLP) and machine learning use and research (Essential); A strong technical background including strong programming skills ((Essential) Knowledge of research techniques and methodologies (Essential
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epidemiology, machine learning, and wet-lab exposure. A tax-free stipend of €25,000 per year is available, and PhD fees are also covered. The research project will also provide adequate funding for equipment
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) and machine learning for liquid biopsy samples (CTCs, ctDNA and EVs) for cancer diagnosis, prognosis and prediction of treatment outcome. The candidate will work in an interdisciplinary team of physical
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working with datasets and conducting data analytics, statistics, using machine learning techniques and/or nonlinear dynamic measures (e.g. entropy, recurrence quantification analysis, etc.).A solid
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renewable generation into dairy farming, by combining it with recent technological developments in the energy sector. This project will investigate a range of topics relating to AI (deep learning
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Technological University of the Shannon: Midlands Midwest | Athlone Springs, Connaught | Ireland | about 1 month ago
record eXtended Reality, Multisensory Multimedia and Quality of Experience within the Faculty of Engineering & Informatics in the midland’s campus. The team now includes more than 20 MSc/PhD students in
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and conducting data analytics, statistics, using machine learning techniques and/or nonlinear dynamic measures (e.g. entropy, recurrence quantification analysis, etc.). A solid understanding of the