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Field
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applied for health prediction in support of prevention. Your core tasks are: · Linking large longitudinal, family, and cohort datasets and preparing these data for analysis. · Analyzing
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(or Generative AI more broadly) with Symbolic AI techniques. In this hybrid approach, the Generative AI component proposes candidate solutions to a problem, while the Symbolic AI component validates
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Computer Science. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component. The
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intelligent systems, all with a strong interactive component. The INtelligent Data Engineering Lab (INDElab) investigates intelligent systems that support people in their work with data and information from
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goal-oriented mindset, and a passion for (scientific) challenges, you are the right person for us. Our lab develops technologies that integrate advanced imaging, computational analysis, and single-cell
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readouts such as Perturb-seq. Experience or familiarity with advanced genome editing methods such as prime editing, or base editing is highly desirable. Experience with bioinformatics data analysis using
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advanced data on nature and biodiversity (derived from AI-supported monitoring and analysis of sources such as satellite imagery, acoustic sensors, and camera traps) can inform spatial planning and decision
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, genes, or metabolites significantly associated with plant phenotypes. These can in turn be experimentally tested by MiCRop partners. You will be part of the Biosystems Data Analysis (BDA ) group
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the analysis of primary material. We will aim for smooth integration of the experimental workflows and data analysis to address clinically relevant biological question in cancer immunology. We aim
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energy planning. This involves exploring how advanced data on nature and biodiversity (derived from AI-supported monitoring and analysis of sources such as satellite imagery, acoustic sensors, and camera