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analysis in cryo-electron tomography data, applied to chromatin organization and synaptic molecular targets. Please include a cover letter with your application. Describe a deep learning project you have
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letter with your application. Describe a deep learning project you have executed, ideally involving 3D image analysis, inverse problems, or physics-informed modeling. Cryo-EM/ET and computational
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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Deployment Strategies - Model Compression: Investigate techniques such as quantization, pruning, and knowledge distillation to reduce the computational and memory footprint of deep learning models without
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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/deploying deep learning models and machine learning applications. Computer skills: Python (PyTorch, TensorFlow), databases (MySQL), 3D Slicer, ITK-SNAP, OpenCarp. Previous experience in research activity in
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the competent authority of the Ministry of Higher Education, Research and Innovation (MESR). "Video content security in a deep learning coding architecture" Over the past few decades, numerous video compression
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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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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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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