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- Fundació Hospital Universitari Vall d'Hebron- Institut de recerca
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- Computer Vision Center
- Institute for bioengineering of Catalonia, IBEC
- Barcelona Supercomputing Center (BSC)
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- CRAG-Centre de Recerca Agrigenòmica
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- Centre de Recerca en Agrigenòmica
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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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. 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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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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), 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
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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
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tasks: Modelling the restings states of novel catalytic materials under relevant experimental conditions. Conducting mechanistic studies to determine the thermodynamics and kinetics of key reaction
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on developing new methods for color-specific conditional generation in multi-modal generative models. The candidate will work on exploiting the intrinsic knowledge about color within text-to-image (T2I) models
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from