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for a doctoral researcher to study how generative AI is changing learning, expertise and relationships at work. The PhD is a four year project that is part of EMANAIRE (Empowering Human Agency in AI
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; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting pathogen invasion success and plant
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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will be responsible for the follow : (full details of duties available from the Job Description) Research Collaboration and engagement You will have completed a PhD in machine learning, computer science
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, including situations where some modalities are incomplete or unavailable. Exploring foundation-model and self-supervised learning approaches for extracting transferable representations from large-scale forest
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decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch energy
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acquire in the course of the PhD skills in live microscopy, experimental neurobiology, genetics and behavioural work. The student will receive mentoring and have the chance to guide the research and
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metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a
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experience or strong interest in power system modeling, optimization, machine learning, and control systems. Documented programming experience (e.g., GitHub projects) in Python, Julia, MATLAB, or similar