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The lab of professor Jesper Tegnér at KAUST has openings for three postdoctoral fellowships in Data-driven Machine Learning for unbiased Discovery of Generative Models with special reference
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and PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental
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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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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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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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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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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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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
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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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Previous experience with molecular cardiology, viral transduction, cell transfections, animal models, immunoblotting, qPCR, cardiomyocyte isolation, microscopy, proteomics, single cell RNA-sequencing