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). / Proven experience in implementing neural networks for radiance fields. LanguagesENGLISHLevelExcellent Research FieldEngineering » Electronic engineeringEngineering » Computer engineeringEngineering
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sequences, networks, trajectories, images, etc. - Design, programming, optimization, and parallelization of machine learning algorithms. - Search in repositories and bioinformatics of DNA sequences
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of the Catalan Aerobiological Network (XAC), Point of Information on Aerobiology (PIA) and Laboratori of Palynological Analysis at the UAB (https://aerobiologia.cat ). Job position requirements (skills/languages
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publish content on social networks, mainly LinkedIn and X. Send notifications to the various subscription lists of the R&D&I Area. Publish on the website: news and events, updating various contents
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Doctoral candidate in the nexus between doping and thermal conductivity in EU Training Network FADOS
Sklodowska-Curie Training Network programme FADOS. The successful candidate will join a cohort of 17 Doctoral students scattered among 16 research groups in Europe and the UK. About FADOS FADOS, Fundamentals
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planetary science, remote sensing, or lunar/space data analysis. Proficiency in machine learning (supervised/unsupervised learning, neural networks, etc.). Strong skills in data processing (Python, R
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in the world of entrepreneurship and entrepreneurial character. Network of contacts of the main national and international agents of the life and health sciences ecosystem. Knowledge of technology
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Research Group and will carry out the following tasks for the research project with code 202234-31, funded by the Fundació La Marató de TV3 PP10656 "Disentangling cliNical, neurObiological, and geneTic
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of interest, together with relevant stakeholders, sister projects, EU initiatives and networks, etc. - Updating the calendar for participation in scientific events of interest according to the internal
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, including: • Processing of organs for histological techniques. Immunohistochemistry and immunofluorescence. • Biostatistical analysis using Python and R tools. • Tomography analysis using neural networks