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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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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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equipment and the newest technology The chance to independently prepare and work on your tasks An exciting work with personal responsibility in the research field of machine learning for material sciences
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Field of study: computer science, mathematics, software design, software engineering, technical computer science or comparable. Machine Learning (ML) models are reaching a maturity level that allows
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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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, 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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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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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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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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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