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project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques, experimental data bases and materials data bases together with novel AI
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equivariant GNNs (e.g., E(3)-equivariance), MACE or related message-passing models. Familiarity with force fields methods. Summary of conditions: Full time work (37,5h/week) Contract Length: 6 months Location
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies
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. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques, experimental data bases and
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Researcher in Structural Analysis for 3D Concrete Printing to join our R&D and Engineering team in Barcelona (Spain). The researcher will play a key role in the design, modelling, and structural validation
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learning) applied to operational infrared images. Design and test a drone payload for the detection and geolocation of fires in night conditions. Collaborate with modeling teams to integrate observational
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experiments and interpret results. Contribute to biomarker discovery and predictive model development. Support data visualization, reporting, and dissemination of findings in publications. Requirements PhD in
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), funded by Ministerio de Ciencia, Innovación y Universidades/AEI, focused on ‘Continual Learning for Foundation Models’ where the aim is to adapt foundation models to new tasks without forgetting previous
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involvement in new projects. This is a full-time position with flexible working hours and a hybrid working model (possibility of remote work). The team offers a collaborative working environment and good
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at CRAG (from basic science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https