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Field
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of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate
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optimization methods for implementation within the framework of the objectives of the doctoral thesis, starting with the exploration of methods based on genetic algorithms. Explore the possibilities
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well as bioinformatics. Preferred or Related Degrees: Biology / Environmental Sciences Preferred or Related Master’s Degrees: Master’s in Fundamental Biology, Genetics and Evolution, Conservation Biology, or similar
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simulation of scenarios with different materials and geometries. - Support the development and implementation of signal and image processing algorithms, including fast inversion techniques, FFT, and nonlinear
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databases. Design, implementation, and testing of deep learning and AI algorithms for processing tabular, genomic and temporal data. Where to apply Website https://www.uam.es/uam/investigacion/ofertas-empleo
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. Specific Requirements Previous experience in chip design, hardware implementation of complex dynamics and learning algorithms, as well as knowledge of memristors, will be highly valued
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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to facilitate perceptual learning of different stimulation patterns; and (iii) the development of advanced AI algorithms capable of converting camera input into real-time electrical stimulation parameters. In
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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric
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Development, implementation and experimentation of distribution estimation algorithms in dynamic optimization problems. Where to apply E-mail lauragveiga@fi.upm.es Requirements Research FieldComputer science