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PhD position: Develop Hybrid Machine Learning for Global Soil Mapping Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity binders that engage with therapeutic targets or efficient (bio
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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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information systems. The work sits at the boundary of machine learning and quantum computation, addressing how quantum devices can remain robust and reliable when operating under real-world noise conditions
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primarily focus on computer vision and machine learning. You will develop and evaluate AI-driven visual speech recognition models and contribute to their integration into a smart-glasses prototype. The
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plant growth, physiology and disease development; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning
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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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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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powders for 3D printing. You will develop the full chain — from waste conversion and powder engineering to additive manufacturing and machine-learning-driven optimisation — enabling sustainable
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affinity for language data; solid programming skills (e.g., Python) and experience with machine learning or NLP, ideally including transformer-based models and word embeddings; excellent English