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for the project. Have documented programming experience in R, Python or other common programming languages. Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning
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analog electronic accelerators. You’ll collaborate closely with a multidisciplinary team of machine learning experts, software developers, computer scientists, fabrication specialists, and experimentalists
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 days ago
simulations and models. Workplan involved construction of chemical networks and analyzing them. Additional topics may include exploring those replicators’ errors. The success of this project will teach us about
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the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning. You contribute to high-quality teaching in bachelor and master years
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and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators. Develop interpretable models
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Institut de Recherche pour le Développement (IRD) | Sete, Languedoc Roussillon | France | about 2 hours ago
analysis, gravity models, Bayesian models, etc.). In this regard, proficiency in software is required: programming languages such as R or Python, machine learning, econometric softwares, data management
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validate adaptive mechanisms for LoRaWAN based on machine learning techniques, targeting improved reliability and energy efficiency in mobile scenarios. To achieve this, it is necessary to go beyond
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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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models remain a limiting factor in moving to a quantitative scale. Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data