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and unsupervised learning, time-series analysis, computer vision, representation learning, anomaly detection, forecasting, or multimodal data processing. The goal is to design and evaluate robust and
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shared goal: to make the world of tomorrow safer. Be part of change Implementation and application of computer vision approaches, including: Application of methods from security-relevant areas such as
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. Your Profile: The successful applicant must have the following: • M.Sc. in natural sciences, electrical engineering, physics, optics, medical technology, biomedical computing, or a related discipline
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imaging systems and ensure the optimal deployment and networking of sensors to efficiently process and analyze the resulting data streams. In this way, we support people in decision-making, process
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MVPs (Minimum Viable Products) and scalable systems. Execution of technology testing and validation processes to ensure market readiness. Interdisciplinary collaboration: Partnering with Product Venture
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improve the sustainability, the energy efficiency and the safety of industrial processes. The Department of Fluid Dynamics Resource Technology Processes is looking for a PhD Student (f/m/d) Experimental
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the next 6 months and are looking for a clearly defined, technically and methodologically grounded research topic. Ongoing studies in computer science, engineering, energy technology, data science, or a
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with clinical PACS systems and DICOM data handling Knowledge of Python and/or MATLAB, especially in scientific computing and image processing contexts Enthusiastic and motivated team player Good
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experience with PyTorch. Ideally, knowledge in computer vision and object detection/segmentation. Motivation to independently delve into new and current research topics. Willingness to work with erotic
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time