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Field
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aims to characterise the sequence, structural, and functional properties of UL-CDRs using deep learning and structural bioinformatics, with the goal of identifying novel antimicrobial peptide candidates
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on large annotated datasets. Memory-efficient deep learning: Model compression, pruning, quantisation, selective memory replay, and efficient training strategies. Energy-efficient deep learning: Methods
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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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imaging using deep learning. You will study the water imbibition in hierarchically porous Si‑based material systems across multiple length and time scales. These systems can manipulate fluid transport
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biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical deep neural networks), multimodal vision and
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
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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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& Machine Learning • Clinical pathways and decision support for patients with acute chest pain • AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis • Speaking
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Particle Acceleration is looking for a PhD Student (f/m/d) Multimodal Reconstruction of Laser-Electron Accelerator Phase Space using Physics-Informed Deep Learning. Your tasks Understand the physical process