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Field
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. We are seeking a candidate motivated to explore the how emerging technologies – such as machine learning, generative AI, and extended reality (XR) – impact societal preparedness planning required
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modelling, machine learning, or microfluidics. They will also have excellent communication, organisational and problem-solving skills, and a strong interest in interdisciplinary quantitative biology
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to develop a new generation of traffic prediction methods, combining traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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, resulting in inconsistencies across soil properties and underperformance in data-scarce regions. This PhD project will develop next-generation machine learning methods for geospatial prediction by integrating
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-cell imaging, multi-omics profiling such as transcriptomics, proteomics, and metabolomics, single-cell and spatial analyses, multiplex biomarker quantification, functional studies, machine learning
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1 Aug 2026 Job Information Organisation/Company Wageningen University & Research Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile First Stage
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closely related discipline). You have a strong interest in AI/machine learning, data mining, regression analysis, responsible AI, causal inference, and programming (R/Python, SQL). Moreover, you are driven
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents