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the ERC project Prometheus. The project assistant's tasks include analysis of primary scientific literature and application of machine learning methods to organize and interpret unstructured knowledge
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professional development opportunities and strive to meet each individual’s development and well-being goals as much as possible. As an associate researcher with expertise in the field of machine learning within
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description The research area is within information theory and channel coding, applied to machine learning algorithms. By adding redundancy, error-correcting codes are used in communication systems to protect
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the AI-Tomo project funded by the Swedish Research funding agency, Vinnova. The project has been established to support and develop the exploitation of machine learning tools to accelerate the analysis
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learning/transfer learning. The subject area includes cellular communication systems for 5G and 6G, channel modeling, channel characterization and machine learning. Work duties The main duties of doctoral
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approaches of genetic epidemiology, molecular epidemiology, pharmaco-epidemiology, and machine learning using large-scale population-based cohorts, national registries, electronic healthcare records, and multi
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. Furthermore, the findings will be followed up with various bioinformatics methods. You will also work with machine learning to develop new methods for classifying diabetes and predicting the risk of
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simulations to develop a machine learning force fields for metal sites that will be used for FEP calculations. We will apply the methods on metalloenzymes of central pharmaceutical interest, e.g. methionine
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hematopoietic cells in general and erythroid cells in particular. Experience working with large genomic datasets. Knowledge of statistics and machine learning applied to biological data. Experience working in a
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relevant subject Additional requirements: at least one course in Programming and one course in Optimization, at least one 2nd cycle course in Stochastic processes, Machine learning, or related subjects