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Field
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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methods Experience analysing RNAseq and/or high throughput imaging data Experience handling human post-mortem tissue Experience writing/using computer code Experience supervising postgraduate students and
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in several areas. These include, but are not limited to, Using MPS to identify new potential targets for future medical countermeasure development Learning development and characterization
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machine learning techniques to enhance source detection sensitivity. Multimessenger modelling and interpretation of Galactic sources. This position offers the opportunity to work at the forefront
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Qualifications Work Experience: Experience with research involving animals. Animal Behavior training and testing. Single unit neurophysiology data acquisition and analysis. Skills: Highly motivated to learn
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processing and machine learning, as well as, online control of assistive systems. The PhD position is located in Aalborg, and the candidate will be a member of Neurorehabilitation Systems group at the Health