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, practitioners and researchers have realized that predictions made by machine learning models should be transparent and intelligible. Although explainable AI methods can shed some light on the inner workings
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years, practitioners and researchers have realized that predictions made by machine learning models should be transparent and intelligible. Although explainable AI methods can shed some light on the inner
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chemistry concepts (desirable). Familiarity with chemical or biological databases (e.g., ChEMBL, PubChem, PDB) is a plus. Experience with Bayesian modelling, transfer learning, few-shot learning, or other
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, stability, safety, and usability under clinically relevant constraints. Perform ex vivo evaluations using soft-tissue models to simulate gastrointestinal navigation and refine the system’s interface and
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collection of wearable sensor data in daily life. Working together with AI experts, you will develop models to extract insights into the progression of key PSP symptoms, based on high-frequency movement and
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. Supervisory Controller Synthesis is an engineering approach to automatically derive a supervisory controller for a given discrete-event model that is safe with respect to certain requirements. Yet, the
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by animal models that optimize for single causes or treatments, without reflecting the diversity and complexity seen in patients. This Marie Skłodowska-Curie Actions (MSCA) doctoral network PhD project
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often proprietary, fragmented, or nonexistent. Despite this, researchers managed to develop generative AI models capable of producing vessel hull designs. Yet, these models face limitations: they often
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Simone Ruggeri. You will also collaborate with a PhD student working at TU Delft, who will use molecular modelling to simulate the milk fat globules and plant lipid droplets and guide the experimental work
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and consequences of localized translation, following up on our recent work (Ramalho et al, 2025, Sci Adv). You will combine ribosome profiling and organoid models with molecular and computational