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Field
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development
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Computer Science, Machine Learning, Operations Research or a related technical field. Strong background in deep learning with a motivation to advance fundamental techniques. Excellent analytical, problem-solving
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analytical and quantitative skills, including proficiency in statistical software (e.g., Python, R). Demonstrated interest in climate change, sustainable finance, and corporate sustainability. Strong written
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related fields; Capability and passion for working on challenging topics that have both fundamental and applied research aspects; Strong competence in analytical skills, mathematical skills, and
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the hiring
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data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning, and artificial intelligence with
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for data augmentation, small language models, anomaly detection using learning methods, and explainability techniques for decision-making. The research will involve designing and developing prototypes
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for Ion Nano-Analytics (AINA) group at LIST, a research team specialized in mass spectrometry. AINA combines expertise in instrument development, nano-analytical techniques, and methodological innovation