22 advance-soil-structure-modelling Postdoctoral positions at University of Cambridge
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-generation AI hardware (ASIC) accelerators. The UK's Advanced Research Invention Agency (ARIA) is supporting an ambitious programme of work that aims to reduce the the cost of AI by more than 1000x: https
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(e.g., Reinforcement Learning, Agent Based Modelling) to join our team full-time as part of a large international collaboration of European researchers (incl. Tobias Dienlin, Veronica Kalmus, Adrian
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complex behaviours, including learning, in the small invertebrate model organism C. elegans. We have recently discovered a range of novel dopamine receptors in C. elegans and found that different receptors
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the coordination of large-scale robot systems (ground and aerial). The ideal candidate will possess hands-on experience with designing and implementing reinforcement learning algorithms, and deploying them onto real
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methodologies. We particularly welcome candidates with advanced knowledge of and a proven track record in hormone biology or root development. Proficiency in plant developmental biology generally, molecular
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that control structure and function at a molecular level in all types of bioactive molecules. We are looking for experts in synthetic organic chemistry who are interested in new challenges and exploiting
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not limited to: electronic structure methods (density functional theory and "beyond"), classical atomistic, and machine learning methods. The duties/responsibilities of this post include developing and
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pipelines will include structural imaging, multi-shell diffusion, susceptibility weighted imaging and functional MRI. These analyses will make use of publicly available tools, and may also require software
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with a large and diverse research team. Previous experience with bioorthogonal chemistry and purification and characterisation of modified biopolymers by advanced mass spectrometry is desirable. We
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modelling to study the causes and consequences of extreme chromosomal instability in these cancers. The role will involve: - Learning and applying cytogenetic methods for generation and analysis of chromosome