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Field
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transfer learning, data-driven calibration, or case-based reasoning, to improve decision-making, reduce uncertainty, and justify steering recommendations? This PhD research together with the DigiWells team
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exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with
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, or other neurodegenerative disorders. Experience with machine learning for large datasets. Experience with computational methods and workflows for handling large-scale data. Personal skills Highly organized
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predictions of various observables as function of cosmological parameters. The candidate will develop and use skills in topics such as statistics, high performance programming, machine learning and using data
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data from live imaging and spatially resolved gene expression profiling. The work of the PhD fellow will be theoretical and computational in nature and will include: Developing minimal active-matter
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statistical and machine learning modeling to conduct data analyses for large-scale multimodal (genomics, omics etc) studies. Conceptualise new ideas, lead data-driven discoveries, ensuring in-depth assessment
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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managing large multimodal datasets, as well as contributing to analytical studies related to machine learning, clinical decision rules, and time-to-intervention evaluations. Responsibilities include curating
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topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant