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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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learning, or human-computer interaction would be advantageous. How to apply We are seeking expressions of interest from qualified domestic candidates who wish to apply for this PhD opportunity. This position
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welcomed. The project sits at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development. Tasks of the PhD Student - Develop and
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. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations from largely unlabelled data that can generalize across
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fusion, machine learning, and systems modelling. We are at the forefront of method development towards large-scale data analysis and modeling of biological systems. Together with a wide range of
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine
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high-resolution mass spectrometry, in vitro pharmacological characterisation of new psychoactive substances, as well as metabolomics and machine learning. As a PhD student, you devote most of your time
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have: MSc in engineering or similar discipline by the start date of the position Experience with mechanical modeling and simulation Experience in computer programming/scripting (e.g., C++, Python, Matlab
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, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine