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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
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/ machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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causal machine learning, transport behaviour analysis, and residential energy demand modelling to support sustainable urban and energy policy. The researcher will contribute to the design, implementation
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combining causal machine learning, transport behaviour analysis, and residential energy demand modelling to support sustainable urban and energy policy. The researcher will contribute to the design
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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
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development of transparent, closed-loop control system for individualized diuretic closing including the validation and advancement of machine-learning and control algorithms, building production-oriented
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computational pipelines for multiplex imaging, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune
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scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas