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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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works have shown that for embedding hierarchies, we should abandon Euclidean geometry altogether and operate in hyperbolic space [1]. Our lab has published multiple papers showing that hyperbolic deep
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Excel, Word and PowerPoint. Strong organizational skills and able to work on multiple concurrent activities. Python programming experience is a plus. EXPERIENCE REQUIRED Undergraduate degree or equivalent
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analytical programming language (e.g., Python, R) or a universal programming language (e.g., C#, Java, C++, Rust) Knowledge of the use of relational and non-relational databases Very good English skills
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patterns across multiple annotation types. The core aim is to generate new scientific insight by associating LCRs with their functions through a combination of expert curation and modern machine learning
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2000 undergraduate majors. The department boasts multiple state-of-the-art core- units with full-time staff. The core facilities include those focused on mass-spectrometry (MS)-based analyses (e.g
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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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, computer science or related fields. Applications must have a strong mathematical background and strong ML/AI background; be able to efficiently implement models in computer code (using e.g. Python and Jax/pyTorch
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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