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Field
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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areas: Developing and training robust machine learning surrogates to replace computationally expensive high-fidelity simulations, enabling exploration of vast design spaces. Formulating optimization
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Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
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, multilevel analysis). Knowledge in developing predictive and forecasting models in health or environmental research. Skills in machine learning or AI techniques for prediction of complex outcomes. Experience
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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, with a view to developing and carrying out the above-mentioned project and related scientific activities, with a particular focus on the development of analytical models (data science – machine learning
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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Functions Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets Modeling plant growth, carbon allocation, stress response (e.g
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supervision signals (e.g., labels in a downstream task or symbolic constraints). You will perform machine learning research, developing a framework for learning interpretable and robust concepts with