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Thesis defended

Miquel Marchena defended his thesis directed by Dr. Blas Echebarria on July 23, 2020. Titled “Modeling pathological effects in intracellular calcium dynamics leading to atrial fibrillation”, the thesis presents a detailed computational model of atrial cell from which different pathological conditions that induce have been studied. atrial fibrillation.

Miquel Marchena defended his thesis directed by Dr. Blas Echebarria on July 23, 2020. Titled “Modeling pathological effects in intracellular calcium dynamics leading to atrial fibrillation”, the thesis presents a detailed computational model of atrial cell from which different pathological conditions that induce have been studied. atrial fibrillation.

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Joan Francesc Gilabert Navarro defends his thesis on computational methods for drug development

Joan Francesc Gilabert Navarro defended his thesis directed by Victor Guallar of the Barcelona Supercomputing Center (BSC) on July 22 at the North Campus. Titled "Estimation of binding free energies with Monte Carlo atomistic simulations and enhanced sampling", the thesis presents the development of a method to predict affinity in protein-ligand systems, with the aim of accelerating the development of new drugs.

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María Masoliver Vila defends her thesis on neuronal coding of weak signals

María Masoliver defended her thesis supervised by Cristina Masoller on February 20 at the Terrassa Campus. Titled `` Neuronal encoding and transmission of weak periodic signals '', the thesis presents a temporal neuronal code based not on the time in which neurons trigger action potentials but on the relative time between them and demonstrates that it is a plausible mechanism for encode information from weak periodic external stimuli.

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Pablo Amil defends his thesis on medical image analysis using artificial intelligence

Pablo Amil defended his doctoral thesis supervised by Cristina Masoller, in the ETSIAAT in Terrassa on February 11, 2020. Titled "Machine learning methods for the characterization and classification of complex data", the thesis presents several automatic learning methods focused particularly on the analysis of ophthalmological images, and complex data in general. The results presented in the thesis show how the proposed artificial intelligence methods are able to distinguish healthy eyes from sick eyes

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