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on well-established model organisms, in our lab we investigate the mechanisms of adaptation in understudied bacterial species. The candidate will use experimental evolution to investigate how bacteria with
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organization. We invite applications from motivated candidates with passion and a strong interest in conducting research and innovation activities on crop modeling, remote sensing, and computer vision to predict
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interdisciplinary training in theoretical language models, clinical approaches, AI methods (e.g., NLP), and neuroimaging, with opportunities to participate in network-wide events and international training activities
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. Analyze the isotopic pathway from precipitation to tree-ring formation. Calibrate process-based models to achieve more reliable climate reconstructions. Doctoral research tasks will include: Bi-weekly
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considered. Mandatory possession of Category D certification to work with laboratory animals and being able to apply for animal experimentation proyects. Experience with cancer mouse models, preferably lung
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. Molecular analysis of different cohorts of lung cancer patients. Animal experimentation (generation of PDX, humanized PDX, xenografts and genetically modified models), use of these models as a preclinical
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Victoria Moreno is seeking a highly motivated PhD student to join our team (http://www.cipf.es/regeneracion-neuronal ). Our group works with preclinical models in search of an effective therapeutic strategy
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health research on Virus evolution using Organoids models (APROVO)” in the frame of the APROVO Project (Proyectos de Generación de Conocimiento, Ministerio de Ciencia, Innovación y Universidades). The PhD
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, aiming to develop models capable of interpreting visual data and responding to clinical queries in a reliable and explainable manner. Where to apply Website https://www.upv.es/entidades/vinv/programas-de
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CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 109772 Research on understanding and optimization techniques for transformer models, with