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Field
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one or more of: computational modeling of social learning, norms, or moral cognition; cultural evolution and gene-culture coevolution; evolutionary game theory and the evolution of cooperation
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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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or datasets are also welcomed. Join an ambitious ERC-funded project and help advance predictive modelling at the interface of supramolecular chemistry, catalysis, and machine learning. The closing date
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analysis Large language models or machine learning/predictive modeling for longitudinal data analysis Strong computer programming skills Strong mathematical or statistical skills Ability to work as a part of
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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in communication theory, signal processing, machine learning, and optimization theory. Strong verbal and written skills in English. Excellent analytical and problem-solving skills, and capacity
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and have synergiccollaborationeffects. Weexpect a motivatedearlycareer researcher with stronginterest and experience with GIS/earth observation/climateprojection data as well as machine learning models
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computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely
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strong foundations in machine learning and artificial intelligence, as well as a solid mathematical background. The position requires a strong interest in exploring multiple research directions toward