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insights into actionable strategies for pediatric care. Our work combines statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data
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discrete choice modelling, behavioural data science or machine learning? Are you interested in developing the next generation of AI tools that accelerate scientific discovery while maintaining behavioural
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innovations. The Pediatric Disease Modeling Lab (https://dbe.unibas.ch/en/research/data-driven-modelling-analysis/pediatric-disease-modeling-lab/) is seeking a Data and Computing Technician to build and
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based on Artificial Intelligence, Machine Learning and Data Science for the modelling, analysis and interpretation of complex biomedical systems. Research activities will include the design and validation
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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machine learning model development, cybersecurity data analysis, experimental implementation, research documentation, and support for scholarly publications and presentations. This position is not
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evaluate statistical and machine learning models - Publish results in peer-reviewed journals Desired Qualifications - Master’s degree in statistics, mathematics, data science, bioinformatics, physics
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Intelligence and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative machine learning models to address key challenges in the future airspace system