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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
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the LAMP group at the Computer Vision Center (CVC), in Barcelona, Spain. The position is for 2-3 years and linked to the project “Foundations for Adaptive and Generalizable Deep Learning” (EXPLORA
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valued. · Knowledge of chemical reactions and how to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials
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Fundació Privada Institut d'Investigació Oncològica de Vall d'Hebron (VHIO) | Spain | about 8 hours ago
. Familiarity with statistical modelling, machine learning and deep-learning Additional information: We offer: 🌐The opportunity to work with our state-of-the-art HPC infrastructure and to join a vibrant network
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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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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apply statistical and machine learning models to identify predictive markers of depression. Design and execute experimental protocols related to self- representation and chronic pain. Prepare manuscripts
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Vision, Language and Reading group at the Computer Vision Center (CVC), in Barcelona, Spain. The position is initially for 3 years and linked to the “European Large Open Multi-Modal Foundation Models
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pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group
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, required to adequately incorporate molecular data, and model regulations of inflammatory and degenerative processes. Available datasets at the molecular level will be incorporated through machine learning