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studies, and computational modelling. They will have the opportunity to visit and work closely with our HFSP partners in Canada (Professor Alastair Simpson) and Denmark (Professor Thomas Kiørboe
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. The position is for one year, renewable subject to satisfactory performance. Successful candidates will conduct research and develop 3D vision. Candidates are expected to have a strong background in relevant
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the knowledge of: 1) deep learning, large AI models, large language models; 2) the rendering techniques for generating human body animations from 3D joint coordinates; 2) understanding wireless transmission
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toxicities, neuron-glia interactions, genetics and epigenetics will provide insights in identifying novel biomarkers and drug targets. Expand and improve skills: disease models based on genome-modified animals
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All candidates and projects will have to undergo a
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Biochemistry Demonstrated expertise in developing 3D protein structure models, protein-ligand docking and bioinformatics Demonstrated experience using protein visualisation software (e.g., PyMol) Demonstrated
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key cancer-fueling pathways in hormone-driven (i.e., prostate and breast) cancers. They will also assess druggability of these genes using novel inhibitory/degrader drugs in preclinical models
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measurements, biogeochemical rate modelling, high resolution 3D-imaging, isotope labelling and integrated geobiological data analysis. Analytical approaches implemented can include a multitude of advanced
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across scales ranging from single bacteria, single host cells, 3D in vitro models to infected hosts. We have developed new imaging technologies to visualise the interface between the host and the pathogen