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can successfully predict previously-unknown regulators of seed development through functional genetic analysis of conserved, uncharacterised genes in the model seed plant Arabidopsis thaliana
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for publication, research seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and
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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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interdisciplinary team comprising chemists with expertise in organic chemistry, reactor design and innovative process analytics, and engineers with skills in fluid modelling, Life Cycle Assessment and sustainability
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models, and animal models of cardiomyopathy, heart failure and vascular disease. During past years, the lab has published in Nature Cell Biology, Developmental Cell, Development, Genome Biology, Nature
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and cancer. Ultimately, we aim to mitigate the pathogenesis and develop treatments for chronic metabolic diseases. We have established unique, comprehensive mouse models for: 1) real-time labeling and
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on the development of a national-scale land use–transport interaction (LUTI) model. This model will be central to the project, generating critical indicators such as job accessibility and population
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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate
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learning to model solid-state materials while collaborating with experimentalists. Qualifications • Ph.D. in Computational Physics/Materials Science, or related field (completed by start date
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Responsibilities: Lead the design, implementation, and validation of data-driven Digital Twin (DT) models to simulate and forecast dynamic energy demands and renewable generation patterns within shipyard operations