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Field
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content is created. Whether it is text, images, or music, generative AI can often produce results that match or surpass results created by humans. However, when it comes to engineering design, and
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Description Two PhD positions in atmospheric selenium research: field & lab analyses and process-based modeling We are seeking two highly motivated and curious PhD students to join the Inorganic Environmental
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and magnetic fields. A crucial aspect lies in the interpretation/modelling of the results. In this role, you will get to work with a variety of devices structures, with an emphasis on quantifying
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biology and bioinformatics, as well as in Machine Learning (including Large Language Models). Good understanding of evolutionary and molecular biology concepts, and good statistical (data analysis) and
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learning models that can be utilised by health services to make real-time, data-informed clinical decisions in youth mental health care. Your key responsibilities will be to: recruiting study participants
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water quality in cities is vital for a healthy city: in this PhD you will develop monitoring and modelling strategies to help design resilient water cities of the future. Job description Cities depend
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to protect AI models against data leakage during inter-departmental information sharing. With the National Police heavily relying on sensitive data exchanges, this research will develop secure machine learning
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data science methods to build explainable and integrated machine learning models that can be utilised by health services to make real-time, data-informed clinical decisions in youth mental health care
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Ensuring good water quality in cities is vital for a healthy city: in this PhD you will develop monitoring and modelling strategies to help design resilient water cities of the future. Job
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decoding of quantum low-density parity-check (LDPC) codes and deriving tighter bounds on the logical error rates of quantum Calderbank-Shor-Steane (CSS)/LDPC codes under the phenomenological or circuit-level