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Field
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the training of state-space models Collaborate closely with our internal partners at PGI-14 (Neuromorphic Hardware Nodes) and international academic and industry partners Publish research articles and regular
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performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators (e.g., Kalman Filter
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Research Assistant Professor - Ohodnicki Lab Mechanical Engineering and Materials Science - Pennsylvania-Pittsburgh - (25004399) Responsibilities: The research-related responsibilities
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
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modeling of complex information systems, and SDU and the Royal Danish Defence College’s established intelligence studies and practice. By introducing computational modeling to traditional intelligence
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of the nonlinear structural performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators
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and what they mean for the system’s efficiency and safety. You will develop models of AI bidding strategies, analyze strategic interactions using game theory, and design optimization methods to identify
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sequencing data and optimise editing conditions Execute pooled functional screens to identify synergistic gene combinations Validate hits with targeted assays and in‑vitro models Contribute to B.Sc./M.Sc
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collaboration that covers all aspects of our research: theory and modeling, sample growth and fabrication, experiments and demonstrations. We have created a dynamic research environment of young and senior
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and