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Field
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integrate calculations of quasielastic and deep-inelastic scattering processes into a unified framework for modeling electron-nucleus cross-section, and evaluate reaction and nuclear-structure models relevant
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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have skills in eukaryotic cell biology, electron microscopy, and bioimage analysis. You have a basic knowledge in integrative structural biology, and in AI / deep learning approaches and/or sub-tomogram
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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candidates will have experience in artificial intelligence, deep learning or decision support applied to RF sensing, wireless communications and signal processing. A strong background in multimodal data
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deep learning approaches, with a particular interest in developing methods capable of handling scarce or corrupted data, designing methods for specific imaging modalities, or understanding and
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Science / Engineering, Electronic Engineering or a closely related discipline, with a good track record of original research publications. The successful candidates will have experience in artificial intelligence, deep
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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning