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Field
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genomic laboratory and analytical methods Perform species distribution modelling and other relevant non-genetic analyses to study species ranges and boundaries Organise and coordinate sample collection
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important to oversee the general robotic testbed development, including the control and sensing architecture for seamless integration of systems from the bottom-up. The operator would require the crucial
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verified with applications from geodynamics. For more information consider the job description here . Tasks Tasks in the project include the efficient implementation of new models, methods, and algorithms
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. Our group combines empirical work (with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets for livestock
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3D Models From Visual Data: Imagine creating 3D photos, holograms, or your own custom video game content from a quick video observation. We develop generative 3D models from 2D or 3D observations
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conjunction with novel linker structures to affect a transformational advance in the molecular design and control of interfacial nanoconstructs with tunable electronic, photonic, biomedical, and chemical
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networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning for inverse
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: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification, building model checkers
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are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random network coding
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experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets for livestock systems in East Africa, and in the subtropics in