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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities
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of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities
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statistical modelling of high-dimensional data, e.g. penalised model selection and machine learning. Demonstrable understanding of RNAseq and gene expression analysis. Experience/skills handling and securely
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approach based on Deep Learning algorithms will be developed and implemented to obtain additional information by coupling the recorded data. Furthermore, the increase in acquisition rates of measurement
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years of postdoctoral research experience is preferred. Strong background in big data analytics, machine learning, and multi-omics. Strong track record of high-quality research, demonstrated by
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of infection using metagenomics, advanced statistical models, and analytical biochemistry. One position to work on an NIH-funded project is available. The project is focused on determining the impact of
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studies; Apply for competitive external funding (e.g., RGC grants) to support a sustainable research agenda; Teach assigned undergraduate or postgraduate courses each semester on communication theory
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such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in
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, substation, corridor scenarios) Integrate physics-informed machine learning models with signal processing feature extraction Develop prototype software tools for automated waveform analytics and real-time