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of Technology, the Netherlands. The mission of the Dynamics and Control Section is to perform research and train next-generation students on the topic of understanding and predicting the dynamics of complex
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support
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. The work is part of the regional project “Optimizing Renewable Energy Integration: FPGA-Based Model Predictive Control (MPC) for Grid Stability” (Ref. SI4/PJI/2024-00238) Where to apply Website https
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mechanisms of adaptive and acquired drug resistance, exploring network-level control and feedback in cell signaling systems, identifying novel drug targets and therapeutic strategies, and developing predictive
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model predictions with biological knowledge and external data sources. Work closely with academic partner groups and the Innovation & Business (I&B) team to align technical development with biological
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modeling, machine learning, or data-driven prediction methods applied to environmental datasets. Experience building and maintaining large, frequently updated archives of weather or climate observations
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test coupons used in HiSOPE RF/microwave PCB & interconnects: Layout controlled‑impedance CPW/microstrip transitions from drivers to OLED fixtures; model launch structures, vias, and ground‑reference
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parameters using experimental muscle and neural recordings Explore motor control policies that replicate observed behaviours Test simulation predictions against muscle ablation experiments Investigate how