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- BCBL BASQUE CENTER ON COGNITION BRAIN AND LANGUAGE
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- BCBL - Basque Center on Cognition, Brain and Language
- Universitat Autonoma de Barcelona
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-year position dedicated to the project “Foundations for Adaptive and Generalizable Deep Learning” (EXPLORA), funded by Ministerio de Ciencia, Innovación y Universidades/AEI, focused on ‘Continual
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learning (R1, FPI) About BSC The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful
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Research Infrastructure? No Offer Description IMDEA Networks Institute, Madrid, is one of the beneficiaries of the MSCA FINALITY (saFe learNIng for lArge scaLe InTerconnected sYstems) Doctoral Network
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23 Sep 2025 Job Information Organisation/Company Universitat de Barcelona Department Genetics, Microbiology and Statistics Research Field Biological sciences Chemistry Computer science Mathematics Other Researcher Profile First Stage Researcher (R1) Positions PhD...
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Machine Learning A PhD position is available at the Computer Vision Center (CVC) under the supervision of Fernando Vilariño and Paula García . The successful candidate will be enrolled in
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to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and electronic
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generate, transmit, and detect OAM-entangled photons under realistic atmospheric turbulence. Deep learning algorithms will be employed to pre-compensate distortions in real time, maximizing state fidelity
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to work in a team -Ability to solve problems independently and through self-learning LanguagesENGLISHLevelGood LanguagesSPANISHLevelBasic LanguagesCATALANLevelBasic Internal Application form(s) needed
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work. Availability for a ≥4 month stay abroad. Background in at least one of: AI/machine learning, computational modelling, microscopy, or cell/molecular biology. LanguagesENGLISHLevelGood Additional
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compositions. Initially, supervised learning models such as random forests, gradient boosting, and neural networks will be used to predict composition outcomes based on both literature scrapping and in-house