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background in machine learning, including Natural Language Processing. You have excellent coding skills in Python; hands-on experience in deep learning frameworks such as PyTorch or Tensorflow is a plus You
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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, criterion handling and machine learning. Topic The main research objective is to contribute to the development of responsible AI, with a strong focus on trust and confidence handling when dealing with data
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. You have a good knowledge of Python and machine learning. You have an excellent knowledge of English. Your research qualities are in line with the faculty and university research policies . You act with
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regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial support for the whole length of the PhD. The applicant will be expected to seek independent
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-type specific samples, state-of-the-art molecular biology techniques, multimodal data generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches
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are looking for a m/f/x Doctoral fellow YOUR JOB You conduct doctoral research in the area of Augmenting Learning Environments Using Generative AI and Neuroadaptive Systems, with the aim to obtain a PhD after
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modeling into modern causal inference by combining its strengths with innovations in debiased machine learning, as well as to improve both the statistical efficiency and robustness of debiased machine
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techniques) at UGent combined with machine learning, deep learning and data fusion modelling to enable development of novel decision support systems for variable rate fertilization and manure application. He