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researchers across the ACT to identify emerging theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating
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and 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
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benchmark their added value against simpler machine-learning baselines; • train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability
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criteria: A PhD (obtained, or nearly completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development
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foundational AI and machine learning models (e.g., multimodal AI, large language/world models) with specific finetuning for ELEVATE; designing geographically context-sensitive urban design recommendations that
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innovation hub” connecting EO with a growing ecosystem of disruptive and transformative innovations such as AI, machine learning, quantum computing, edge computing, metamaterials and photonics. Many of the
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collaboration with the industrial partner PhotonFirst and aims to quantify model parameters and their variations as such providing data for the Physic-Informed Machine Learning model. Your profile The
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preferred candidate; there is budget for support in technology development. You will work closely with a PhD researcher at the German partner who focuses on the underlying machine learning models, and
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characterize subsurface structures, buried infrastructure, and material properties; · develop and apply AI and machine learning methods for signal processing, image analysis, data fusion, and prediction
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intelligence; demonstrable machine learning skills; curiosity about societal issues and how AI can be employed for the benefit of the public; strong teamwork skills, as collaboration with other researchers