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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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Fundació Privada Institut d'Investigació Oncològica de Vall d'Hebron (VHIO) | Spain | about 17 hours ago
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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of research protocols, SOPs and related documentation · Be responsible for the recording, documentation and reporting of all preclinical models used by the Nanomedicine Lab · Perform cross-faculty collaborative
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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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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