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Field
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, genes, and pathways involved in sensing water in the model plant Arabidopsis. Building on our previous work (Mehra et al., Science, 2022), we aim to understand how hormone-driven pathways help roots
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works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include
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an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary
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on innovative design for legal and regulatory systems for AI and other complex global technologies; computational models of human normative systems; and working with machine learning researchers to build ML
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technologies such as low drag laminar flow nacelles, nacelle drag sensitivity to manufacturing tolerances, as well as low order based models to enable rapid preliminary assessments. Furthermore, it is envisaged
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, and software development, modelling and simulations) involving instrumentation for or astronomical datasets from ground or space-based telescopes. Ability to engage with the wider astronomical community
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group selection management to promote structural complexity, making them a useful test of different modeling approaches in the Forest Vegetation Simulator (FVS). Currently FVS users, which largely consist
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molecular techniques in cultured cells, xenografts, and engineered mouse models • Working with conventional laboratory reagents, instrumentation, and molecular/tissue biology techniques. • Operating and
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be proficient in both written and oral English. Experience from one or both of the following areas is an advantage: Modelling and simulations of flow in porous media. Programming, image processing and
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healing. The Granville research program spans basic molecular biology and biochemistry through to target validation, proof-of-concept in animal and human models, and collaborations with clinicians and