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Field
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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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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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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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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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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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behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design a public transport network that includes both traditional fixed lines and flexible on-demand services, while
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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
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medicine with seemingly endless possibilities to match the right treatment to the right patient. As organoid data are often longitudinal in nature (e.g. expressed by growth curves), can be derived from
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for an ambitious PhD student on perceptual foundation models. Your research is part of the Video & Image Sense Lab. Join our team! Recent breakthroughs in Artificial Intelligence resulted in the emergence