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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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science in carrying out concrete AI projects. This includes compiling, organizing, and sharing key datasets, assisting with resource allocation proposals, conducting machine learning workflows, and
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sciences in carrying out concrete AI projects. This includes compiling, organizing, and sharing key datasets, assisting with resource allocation proposals, conducting machine learning workflows, and
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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performance in organic electronic and electrochemical devices. Multiscale simulation and integration of machine learning: Use molecular dynamics, quantum mechanical and continuum models, in combination with
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in programming machine learning models and hardware for image acquisition is particularly meritorious. Since teaching and research are largely conducted in English, you must be able to demonstrate
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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if you have worked with prediction models, machine learning or AI models and are familiar with blood cells such as neutrophils, leukocytes and platelets. Work experience in the area is meritorious. If you
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consist of the following: Mathematical analysis of ecological and eco-evolutionary models, involving pencil-and-paper calculations; Computer simulations of more complex models which do not easily lend
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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate