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development and application of novel algorithms and machine learning/AI techniques for extracting insights from biological data sets (genomics, proteomics, imaging, neuroscience), and related areas
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to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization
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both the algorithms for robot co-design, and the real-world evaluation of the designs that emerge. You will investigate evolutionary algorithms to explore creative new hand designs, and reinforcement
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minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
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Responsibilities Computational Modeling & AI Development Train and fine-tune ESM (Evolutionary Scale Modeling) protein language models on curated datasets to generate novel protein sequences Develop and optimize
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of algorithms and models to realistically simulate forest ecosystem dynamics under varying conditions of land use change, forest and land management, climate variability, and other environmental stressors
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of algorithms and models to realistically simulate forest ecosystem dynamics under varying conditions of land use change, forest and land management, climate variability, and other environmental stressors
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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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expertise in machine learning, deep learning, audio/image/video classification, attention mechanisms, zero/few shot learning, and evolutionary algorithms. The Associate should have proficient programming
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, algorithms and systems architecture. Interest in functional programming and other programming paradigms is also relevant. ETL, data wrangling and data analytics Competence in mathematics/statistics