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Field
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lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data
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understanding of intersectoral scientific fields ranging from optical fibre communication design and implementation, components fabrication, artificial intelligence, or techno-economic analysis, which represent
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range of science and engineering disciplines, from classical disciplines such as mathematics, astronomy and mechanical engineering, to interdisciplinary fields such as artificial intelligence,nanoscience
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, engineering, mathematics or a related field of study Huge interest in data science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in
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and/or interest in the following relevant fields: artificial intelligence and cybersecurity. The candidate will have the opportunity to work on a collaborative project with a leading industry in
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(Computational) Linguistics, Education, Artificial Intelligence or a related field; The skills and knowledge for in-depth automated text analysis and the development and use of text quality metrics; The skills and
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and networks worldwide to become a truly global centre of knowledge. This 4-year PhD position is embedded in the Artificial Intelligence department at the Faculty of Science and Engineering and in
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Your Job: As a PhD candidate you will develop and deploy an artificial intelligence (AI) driven approach to streamline high-throughput experimentation (IMD-3: Institute of Energy Materials and
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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drug design” is led by Docent Juri Timonen, at the Division of Pharmaceutical Chemistry and Technology. Our aim is to create new machine learning and artificial intelligence methods to accelerate drug