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processing, machine learning, and optimization theory. Strong verbal and written skills in English. Excellent analytical and problem-solving skills, and capacity to pursue independent research as
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time scales. To do this, we will build on a landscape picture of stochastic gene expression dynamics inferred from data using modern machine learning techniques. The results will inform us about how
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for the efficient training and fine-tuning of machine learning models. The postdoc will closely collaborate with researchers at the Dutch Language Institute (and Radboud University Nijmegen). Selection Criteria PhD
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in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications Experience in
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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skills in remote sensing, geospatial data analysis, artificial intelligence or machine learning, and environmental or agro-meteorological modelling, as well as experience handling large Earth observation
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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required. The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, led by Laura Cantini, works at
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will focus on developing efficient foundation models to medical image analysis. Foundation models offer a scalable and adaptable solution for medical image analysis by learning generalizable
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research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models