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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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intelligence as applied to trauma systems and acute care surgery. Fellows will engage in cutting-edge research spanning multiple domains, including risk prediction models for surgical complications, clinical
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Control - Resilient autonomy for Self-Healing Soft Robotic Platforms - Uncertainty-Aware and Predictive Human-Robot Interaction Qualifications To be qualified for the position, you must have a MSc degree in
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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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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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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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areas providing a template for relevant directions: - Embodied Intelligence for Soft Robotic Systems - Foundational Models for Adaptive Soft Robots - Real-Time Adaptive and Stiffness-Aware Control
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intelligent decision architectures, predictive analytics, and adaptive computational models that can operate in dynamic, uncertain, and high-stakes project environments. The appointee will conduct original
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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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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