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At the Fraunhofer Institute for Laser Technology ILT, we may not be developing lightsabers to fight the dark side of the force, but our innovationsoften sound like something out of science fiction
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advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data, with a focus on identifying critical transformation windows and assessing phase evolution kinetics
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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid
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: Equivalence checking of quantum circuits. This task will include the identification of suitable metrics for approximate equivalence and algorithms to efficiently compute approximation distances. A potential
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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algorithm to reduce noise in dual-tracer PET imaging and improve the quantitative accuracy of dual-tracer PET scans. Your Job Simulation of PET data using existing simulation packages. Development
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SHARE@NTU Lab is developing key technologies for humanoid robot research, designed for intelligent service, industrial assistance, and human-centric environments. The key technologies include
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Student (f/m/d) in Quantum Algorithms for Droplet and Bubble Oscillation Dynamics Modelling. Your tasks Development and implementation of numerical and algorithmic methods for the simulation of fluid
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settings. Develop and test algorithms for object detection, tracking, and classification using wireless sensors. Help guide and mentor graduate students and other junior team members working on the project
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available in the further tabs (e.g. “Application requirements”). Programme Description The research and training programme focusses on the mathematical and algorithmic foundations of reliable AI along with