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experimentation, modelling, and noise‑control strategies across systems such as airfoils, ducted propellers, drones, and wind‑energy devices. With strong academic and industry partnerships, our group tackles
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contract model are those approved under the University of Coimbra's Research Grant Regulations. Where to apply Website https://apply.uc.pt/ Requirements Research FieldOtherEducation LevelMaster Degree
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financial planning and predictive modeling to inform strategic growth, program viability, and resource allocation. Set Continuum-level financial targets and guardrails (e.g., administrative budgets, reserves
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attention to scientific rigor and interpretability Experience with XAI tools (SHAP, LIME, Integrated Gradients) to identify which features of the model are driving the predictions Clear written and verbal
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the integration of behavioural data with AI. The student will analyse eye movements, exploration patterns, and verbal reports to develop computational models that predict identification reliability
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient
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the direction of the Principal Investigator in building a first-of-its-kind Software as a Medical Device (SaMD) that predicts, detects, and manages SSIs by fusing RGB + thermal wound images
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Optimization (AI/ML) Developing AI/ML models to predict drillability issues based on mechanical rock properties Real-time parameter optimization (WOB, RPM, flow rate, etc.) using machine learning techniques
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apply AI and data-driven modelling to predict system efficiency - balancing air purification with energy consumption. It will also explore how sensor feedback can control treatment systems and communicate