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simulations of compact binaries (including, for example, binary black holes, binary neutron stars, and black hole–neutron star binaries). The broader goals are to generate accurate predictions for gravitational
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or predictive modelling, edge AI, AI for biomaterials formulation, processing and manufacturing optimization. Wearable devices – wearable physiological sensors, smart textiles, soft robotics, and exoskeletons
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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Charité–Universitätsmedizin Berlin (Dr. Rosanna Sammons); for further information, see https://www.sfb1315.de/ - development of network models of the CA3 region of the hippocampus - investigation
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, as well as from industry. The successful candidate will work in the established collaboration between DSB and ICGI to develop multimodal deep learning models for predicting prostate cancer
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quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution
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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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, displacement risk through gentrification, right to counsel, and pervasive institutional ownership of single-family homes. This work continues with the need for regulatory models, innovative financing strategies
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, AtomGPT). Working Knowledge Of: • Workflow tools (e.g., ASE) and HPC environments. • Software development in Python, Git-based version control, and Conda packaging. • Data integration and surrogate modeling
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interactions. This involves (i) developing predictive machine learning models that forecast user actions and remote system responses across audio, video and haptic modalities, and (ii) jointly orchestrating