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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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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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of existing studies to promote the use of risk-informed decision frameworks, prediction models, AI applied to planetary protection. Tasks include: Support the creation of probabilistic models for planetary
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17 Mar 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Modelling tools Geosciences » Geology Geosciences » Hydrology Researcher
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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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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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learning, generalization/robustness and privacy aspects in scalable learning algorithms. Large‑scale optimization and control: Optimal control, model predictive control and other optimization‑based control
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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
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plus. Experience with advanced analytics, including predictive modeling, data science, or statistical analysis to support data-driven decision-making. Demonstrated experience designing and implementing