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Field
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conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU Solutions at the UM6P Data Center. Innovate and improve image analysis algorithms for plant trait quantification
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across domains. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data
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methods to improve the deployment, adaptation capabilities and safety of robots and critical infrastructures. The developed algorithms will be evaluated on legged robots, wheel-based robots and under
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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across domains. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data
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genetic knockouts in yeast and mammalian cell lines, and protein purification. Job Responsibilities: 35%: Computational algorithm development and data analysis 35%: Design and conduct experiments with yeast
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data sources such as UK Biobank and eventually come up with algorithm useable for the early detection of Alzheimer’s disease (AD) and Parkinson’s disease (PD). Nature of Work: In this project, we will
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and