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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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experience with scientific computing, data analysis, machine learning and/or AI You have an interest in environmental sustainability and pharmaceutical production Considered a plus: You have experience with
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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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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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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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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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programming. You are highly motivated to conduct (applied) research at the intersection of (deep) machine learning and the health sciences. You have good programming skills in languages such as Pythorch, and
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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