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, to achieve spatial understanding and cross-modal representation learning from heterogeneous sensor data, with the research not limited to these methods. This research will support semantic interpretation
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and understanding of complex biological systems and biodiversity. You will get the opportunity to learn about both simple and complex biological models, computer programming, data visualisation, and
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if you have any of the following: Prior experience with inverse modeling or parameter estimation. Experience in microscopy or microfluidic lab environments. Experience with open-source codes
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the domain of AI and optimisation, and develop methods to defend against such harms by leveraging cryptographic approaches. To this end, you will: 1. Investigate how to adapt existing adversarial attacks
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, updated with anti-nutrient content (phytate, tannins, oxalate), to apply and further develop mathematical models to estimate the absorbable micronutrient content from meals and dietary patterns. You will
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and analysing general Computer Vision methods, using real-world image data as a challenging experimental setting. Research focus The PhD will address open research questions in Computer Vision related
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vision imaging technologies and machine learning methods to estimate physiological parameters (eat-readiness, shelf-life) of fruits at industrial sorting speeds. The work will include creating an accurate
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(neural mass models) as well as at the neuron level (neural network models) including plasticity. Electric fields will be estimated based on finite-element method models. The project can be partly adapted
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Experiment (PRIDE) Doppler/VLBI data: open-source analyses in orbit estimation and ephemeris improvement. Job description We are seeking a highly motivated PhD candidate to strengthen the scientific
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19 Feb 2026 Job Information Organisation/Company University of Twente (UT) Research Field Computer science » Informatics Educational sciences » Education Educational sciences » Teaching methods