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DTU Tenure Track Researcher on Nanoreactors for Operando Visualizations of Nanoparticle Catalysis...
microscopy for nanoparticle catalysis at the Center for Visualizing Catalytic Processes (VISION). Responsibilities and qualifications You will engage with VISION’s development and application of nanoreactors
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ICT Services & Applications. Your role The SnT Automation & Robotics Research Group seeks to hire an excellent and motivated PhD candidate within the national research project PCS-GRAPHS (Integrating
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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of error-controlled biomechanical models in SOFA / FEniCSx / SOniCS for real-time use on AR devices Design of Bayesian neural-network surrogates and graph-based models for tissue deformation and brain shift
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dataDesigning hierarchical graph‑based models to predict toxicity under uncertainty by linking molecular‑level and system‑level knowledgeAdvancing causal inference methods to predict transformation products under
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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep
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learning analysis of biomedical data and bioscientific programming for projects on neurological diseases. The candidate should have experience in the analysis of large-scale biomedical data (omics, clinical
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biomaterials, (9) Data-driven materials development, and (10) Advanced materials analysis technology. R26-01 Materials Science R26-02 Materials Science (for women) - Researcher [Field specified]: one position
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estimation, and the detection and compensation of sensor drift and degradation. The candidate will develop data processing and modelling approaches combining signal processing, statistical analysis, and data
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
analysis of parameterized finite‑element models. By integrating data‑driven and FEM‑based approaches, a hybrid model is developed that accurately represents physical relationships while providing real‑time