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learning for decentralized AI model training for tool wear detection and measurement in milling processes within the »FL4AI« project. A custom dataset has been acquired, consisting of microscopic tool wear
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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student assistants and contribute to shaping the CRC’s research direction Your Profile PhD in computer science, neuroscience, machine learning, or related field Strong programming skills in Python and
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for multimodal inferences, combining computer-vision, environmental parameter measures and DNA data. Your role will be central in data acquisition and foremost machine-learning models creation. You will
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, and therapy resistance mechanisms Ability to work independently and collaboratively within interdisciplinary teams Prior experience with network modeling or machine learning is a plus We offer
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Interaction Causal Models and Inference Time Series Modelling Multimodal Data Integration and Modelling Image Recognition and Computer Vision Computational and Simulation Science Visualisation High-Performance
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founded research area of "Digital Technologies" with a focus on computer-aided high-throughput methods and AI-supported model development presentation of scientific results at international conferences and
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electrochemical impedance spectroscopy (EIS) directly during the disassembly process to classify the cells for their reusability. A pre-trained machine learning model for assessing cell condition based on EIS data
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learning models Investigation of these models in light of recent advancements in Selective State Space Models (SSMs), aiming to bridge the dynamics and working principles of SSMs with the dendrite-augmented
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be