92 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions in Netherlands
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, multi-modal data, and GPU-accelerated machine learning for materials science. Information We are seeking two highly motivated postdoctoral researchers to join the Horizon Europe project SIMU-LINGUA, a
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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming
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a national and international scale, yet small enough to offer a personal and engaging learning experience. In this way, we contribute every day to a safer and more sustainable world. Impact with law
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27 Feb 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint
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to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high
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models that provide evidence-based reasoning for mission-critical decisions. Explainable AI for mission-critical decision support: design interpretable machine learning architectures capable of offering