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. The focus is on developing AI-supported, sensor-based solutions for real-time monitoring and optimization of manufacturing processes. These solutions aim to assess process conditions during production
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utilization, adapt or modify algorithms to run more efficiently and consult, train and support our users on best practices along those directions. You are expected to take a holistic approach considering
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, and integrated analysis. The team runs an excellent open source based software environment and establishes state-of-the-art data analysis concepts and algorithms. Job description The bioinformatician
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proven experience in FPGA programming and high-speed electronics circuit design and testing. Experience in quantum information processing and quantum communication algorithms, as well as fiber optics
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institutions, to build a framework for discovering, sharing and executing data and algorithms in a distributed environment. The main technology will be Python, though Scala and Typescript knowledge are a plus
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systems. Your work will help to define the quality and features of our algorithms. Armed with your innovative spirit and project experience, you will manifest fresh ideas and novel approaches
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generation of enhancement algorithms tailored for endoscopic videos, with a focus on applications in real-time interventions. Development efforts will prioritize both enhancement quality and computational
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) or neural network-based methods. The level of the targeted problems will require further mathematical and algorithmic developments over the current state of data-driven SSM reduction. The PhD position will
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. Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
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temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to better understand the tradeoffs related to cooling technology