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preparation and testing or powder mixtures, and then to devise predictive models (possible using machine learning approaches) for the estimation of mixture properties from pure component propeties. The PhD
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this goal, doped-diamond systems will be considered. The thermal stability of selected compounds under operating conditions will be assessed by means of molecular dynamics simulations with Machine Learning
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to learn innovative approaches in data analysis, including the programming, AI – tools, machine learning Mobility condition: - the candidate MUST NOT have their main activity (residence/work/studies) in
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Machine Learning. Profile of the graduate The graduate displays deep theoretical knowledge in molecular and cell biology, genetics and virology, with focus on some specific branch of these scientific fields
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of dissertation topics: Developing Remote Sensing–Based Indicators of Landscape State and Change Using Data-Efficient Machine Learning Across Scales Profile of the graduate The graduates have deep theoretical
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: an insight from Genetics, Single-Cell Transcriptomics, and Machine Learning. Profile of the graduate Ph.D. graduate has extensive knowledge of cell and developmental biology, ranging from basic principles