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the framework of the PHYNEST project. PHYNEST is a prestigious new MSCA-COFUND doctoral programme coordinated by the UAB, designed to recruit and train 25 doctoral candidates. The programme focuses on advanced
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to recruit and train 25 doctoral candidates. The programme focuses on advanced materials, innovative methodologies, and transformative solutions that address today’s most pressing challenges in the fields
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advanced materials with tailored functionalities, exploiting structural control at the atomic and nanometric level to engineer enhanced thermal and electronic performance. Our research spans organic and
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conferences (punctuation: 20) Item 03: Advanced knowledge of experimental data analysis in R, including mixed models (LMM and GLMM) and survival models (Cox) (punctuation: 15) Item 04: Previous experience in
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doctoral candidates. The programme focuses on advanced materials, innovative methodologies, and transformative solutions that address today’s most pressing challenges in the fields of energy transition and
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of databases and data visualization. - Design of a timber architecture guide and a manual for the use of hardwoods. - Execution of experimental tests for the characterization of structural components and
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of mixed-oxide catalysts for application in chemical looping processes aimed at energy transition and industrial decarbonization. The work will involve Advanced synthesis of mixed oxides, doped systems, and
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and international environment, with training in AI, microscopy, and computational structural biology, and will benefit from established collaborations with the Soft Matter Lab (University of Gothenburg
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- Molecular Modelling and Bioinformatics Dr. Núria López-Bigas - Biomedical Genomics Dr. Patrick Aloy - Structural Bioinformatics and Network Biology Dr. Raúl Méndez - Translational Control of Cell Cycle and
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FOR DRAWING UP OF PREDOCTORAL CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119865 Development of machine-learning and graph-based models