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Your Job: This thesis focuses on designing, evaluating, and deploying algorithms for robot perception and control. The main task is predicting both self-motion and the motion of surrounding agents
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that process planning has a high potential for automated optimization. Building upon this, you will advance our optimization pipeline and evaluate different optimization algorithms/strategies. What you will do
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multi-parameter ion-beam tuning procedures (collaboration with Univ. of Vienna and HZDR) and developments of machine learning (ML)-algorithms for optimization of beam parameters and control of relevant
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will work in a group with other students and take on specific tasks. The aim is to analyse the robot's capabilities and to implement algorithms that enable the robot to be used sensibly in applications
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you will do Driving innovative AI research through the development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine
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) simulations and offer time-saving benefits. We are looking for a dedicated and motivated student to assist us in implementing a novel Graph Neural Network based algorithm that can act as surrogate for FEM and
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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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structure, efficiently solving problems in HOBO ("higher order binary optimization") formulations, or exploring Grover-inspired algorithms and Quantum Imaginary Time Evolution. What you bring to the table
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Your Job: In this Master’s thesis, you will investigate the impact of different programming algorithms on the stability of resistance states using a sophisticated 3D Kinetic Monte Carlo (KMC) model
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control, state estimation, and path planning algorithms for single and multi-agent robotic systems (UAVs). develop and train AI models for practical applications such as real-time object detection and