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» Informatics Computer science » Modelling tools Mathematics » Applied mathematics Mathematics » Algorithms Mathematics » Computational mathematics Mathematics » Statistics Physics » Statistical physics Physics
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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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achieved by combining state-of-the-art algorithms from multiple domains such as evolutionary algorithms, reinforcement learning, and control theory. The main responsibility of the successful applicant will
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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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needs, such as assisting the team with evaluating evolutionary algorithms for exploring creative new hand designs, or reinforcement learning for policy optimisation, all within a huge GPU-based simulation
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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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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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