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Chair Prof./Prof./Associate Prof./Assistant Prof./RAP in Faculty of Computer & Mathematical Sciences
-class university that makes a positive impact on society, living up to the University's motto: "To learn and to apply, for the benefit of mankind". Ranked among the top 100 universities globally by well
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. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval
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Engineering and Mechatronics. The Proposed PhD thesis topic: “Intelligent Diagnostics of Electrical Machines through AI-Enabled IoT Systems: Design of Custom Embedded Hardware and Protocol-Aware Architectures
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parallel, it produces new, high-resolution computer models of the warm Last Interglacial period. Finally, PAST creates new knowledge by synthesising these two approaches through advanced statistics. This PhD
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. 2012; 24(6):634-9. https://pubmed.ncbi.nlm.nih.gov/22960555/ Research area: Cancer biology Keywords:CLL, microenvironment, CD20, BTK/PI3K inhibitors Funding for the PhD candidate: In the academic year
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active materials by making use of artificial molecular machines. SPRING will establish innovative concepts to elaborate (i) active (supra)molecular systems, (ii) new synthetic objects to study some
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computational analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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: RNPUs, Nature 577, 341-345, 2020 . In this PhD project you will work on applying RNPU networks for solving computational problems that are considered hard. Where to apply Website https
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University of Savoie Mont Blanc (USMB) that brings together expertise in machine learning and information fusion, as well as networks and systems. It develops methods for processing and managing data in
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reclamation pilot-scale and lab-scale systems. Conduct membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning-based