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datasets with multiple omics layers (including genomics, proteomics, metabolomics, and microbiomics) to uncover how environmental factors shape human health at the molecular level. You will collaborate
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study the water imbibition in hierarchically porous Si?based material systems across multiple length and time scales. These systems can manipulate fluid transport through the tailoring of their porosity
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characterisation techniques is beneficial, but not required; Experience in at least one programming language for scientific computing (C/C++, Matlab, Fortran, Python, Julia, ...); Good communication skills and
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modelling; Knowledge of spectroscopy is beneficial, but not required; Experience in at least one programming language for scientific computing (C/C++, Matlab, Fortran, Python, Julia, ...); Good communication
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generation sequencing Applying machine learning-guided directed evolution to improve multiple enzyme properties Upscaling selected biotransformation reactions in collaboration with academic partners
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geometry altogether and operate in hyperbolic space. Our lab has published multiple papers showing that hyperbolic deep learning has strong potential for computer vision, from hyperbolic image segmentation
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in Mathematics, Computer Science, or Statistics Proficiency in one or multiple programming languages (R, Python, Julia, MATLAB) Interest in infectious disease dynamics, public health, global health and
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equivalent in Mathematics, Computer Science, or Statistics Proficiency in one or multiple programming languages (R, Python, Julia, MATLAB) Interest in infectious disease dynamics, public health, global health
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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relevant for wafer handling. Silicon is a very complex material, undergoing multiple phase transformation, with some phases showing extensive plastic deformation, during mechanical loading and contact