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deep learning, on the topic “Analysis and Reconstruction of Digital Data Fragments”. This internship is intended for students at M2 level, or in the final year of an engineering school, interested in a
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bring: A PhD or equivalent qualification, with a distinguished record of leadership in teaching and learning at the tertiary level. An internationally recognised and sustained track record of impactful
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physical models (including dispersion forces, magnetic effects, and ligand–solvent interactions), and train modern deep-learning methods to create smooth and reliable energy landscapes. A key goal is predict
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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challenging and impactful research and development programs in healthcare informatics, bioinformatics, high performance computing and deep learning. We have a collaborative environment focusing on designing
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Architecture Search (NAS) that can automatically design efficient deep learning models optimized for specific embedded hardware platforms. These models will be deployed in resource-constrained, standalone
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with artificial intelligence (machine learning/deep learning) Essential Application/interview Experience with classical image processing techniques (e.g. classification/segmentation/registration
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staff working in the R&D unit. Where to apply Website https://jobs.energy.imdea.org/es/offer/394 Requirements Research FieldEngineering » OtherEducation LevelPhD or equivalent Skills/Qualifications PhD in
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PhD in in a relevant field would be an advantage. Deep understanding of the needs of a diverse student population, including students from equity backgrounds, and contemporary issues impacting equitable
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LiDAR data (GEDI and ICESat-2), and generate time series; e) Apply deep learning (DL), machine learning (ML), geospatial foundation models (e.g., AlphaEarth, TESSERA), statistical inference (uncertainty