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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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at: www.ftf.lth.se , www.nano.lu.se , https://kaw.wallenberg.org/en/research/semiconductor-bandgap-key-future-green-electronics, https://c3nit.se/ Subject description The purpose of this project is to develop a data
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of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware coordination of electric vehicle fleets
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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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. Conduct transportation resilience assessment and enhancement studies based on GIS, complex network analysis, and machine learning. Simulate human mobility in response to extreme weather events (e.g
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research. Teaching may also be included, but up to no more than 20% of working hours. The position shall include the opportunity for three weeks of training in higher education teaching and learning
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20% of working hours. In connection to this, the position includes the opportunity for three weeks of training in higher education teaching and learning. The postdoctoral fellow will: Develop and