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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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: Cambridge, Massachusetts 02139, United States of America [map ] Subject Areas: Biology / Biodiversity , Cell Biology , Ecology , Evolutionary Biology , Microbiology , Molecular , Molecular/Cell Biology
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), National Animal Disease Center, Virus and Prion Research Unit, located in Ames, Iowa. For an introduction to the flu-crew at the National Animal Disease Center, please see: https://youtu.be/kOJy8tFTuiI
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models optimised with evolutionary algorithms to address combinatorial optimisation in model design and the noisy nature of climate data. The Doctoral Researcher will receive on-the-job training in machine
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of Device Bioengineering, including but not limited to the development and implementation of approaches that interface with living systems through novel materials and algorithms, electric and magnetic fields
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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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Machine Learning. Work plan: Review of the state of the art on Evolutionary Algorithms and image tampering detection; Implementation of an evolutionary algorithm for image tampering detection
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) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain together. • Apply quality-diversity methods to discover a wide range of high-performing designs
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Interatomic Potentials) code for ternary compounds with variable composition with crystal structure optimization algorithms (evolutionary, random, etc.); - Application of the CSP DFT/MLIP methodology to various