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Field
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and high-speed microscopy with AI and machine learning to form stable liposomes from libraries of (novel) phospholipids that can robustly encapsulate cell-free gene expression systems. You will then
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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modeling, machine learning, • Experience in human electrophysiological research is a plus, experience in intracranial human research large plus, • Knowledge of cognitive system is a plus, knowledge of
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scale. Increasingly, our work integrates AI and machine learning approaches to process and interpret large and complex Remote Sensing datasets. When joining our group, you will also join the wider and
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neuroscience. This requires programming of experiments with human participants, typically in computer-based experiments in a neuroimaging or behavioral and stress lab setting, but also in online experiments. You
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will begin from June 2026. Education and Background PhD degree in a relevant quantitative discipline such as Bioinformatics, Statistics, Mathematics, Data Engineering, or Biomedical Engineering
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technologies including semantic/task-oriented data processing, signal processing, and network resource management to improve the performance of future wireless communication systems. Finally, due to the large