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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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discover them The Opportunity The Department of Electrical and Computer Systems Engineering is seeking a highly skilled Research Fellow with a strong understanding of radar radio frequency (RF) systems
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methodologies like machine learning, unstructured data analysis, etc., applied to substantive marketing topics like social media, digital advertising, digital platforms, etc. In addition, we would be keen to hear
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, virtual screening, molecular docking, structure-activity relationship analysis, and machine learning. Candidates should embrace opportunities to tackle new problems and challenges as part of a dynamic team
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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computer literacy, particularly with current project management software Experience in finance or sustainable finance, or a strong interest and proven ability to quickly grasp key issues within the industry
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, Engineering or others related to the PhD topic) Excellent programming and/or robotics background, with a keen interest in human-robot interaction Prior knowledge of robotics and machine learning (e.g., relevant
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computer skills with experience using Microsoft Word, Excel and PowerPoint; and specific experience in working with a range of data management/analytical software. Prior experience, knowledge and expertise
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to advance computer skills are desirable, but not essential. A valid driver’s license and willingness to drive to general practice clinics, patient homes and other care settings, will be required. This is