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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions
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research and soft robotics development. PhD project The PhD project will focus on the technical aspects of simulating the physics of the Drosophila larva body. The primary objectives include: Developing a
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Computer vision (3D, detection, classification, segmentation, object tracking). Autonomy, creativity, and interest in real-world AI applications. Interest in agriculture or agri-food. What We Offer
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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novel multi-objective optimisation algorithms, to evaluate metrics such as material circularity, system efficiency, cost, and carbon footprint. The University of Surrey is ranked 12th in the UK in
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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency
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techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques
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Department at Monash has a thriving research culture, and is ranked 33rd in the world in the latest QS rankings. Candidate Requirements The successful applicant will have an excellent academic track record in
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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. You will draw on ideas from Bayesian optimization and Bayesian deep learning, generative modelling, high throughput screening, and combinatorial synthetic chemistry. Responsibilities and qualifications