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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, GenAI, and IoT/5G on organisations from both the private and public
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experiments. Data & Analysis: • Collaborate with data scientists to analyze host and microbial data using statistical, bioinformatic, or machine learning approaches. • Contribute to the integration of spatial
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specific drug resistance and pathogenesis mutations. The project will combine classical microbial genomics with machine learning and AI analysis approaches to create the most in depth population analysis
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, collaboration, and effective teamwork between human workers and AI agents. The research will employ Participatory Design (PD) approach, emphasizing a democratic approach to technology design. This will require
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Simulations (LES). The analysis will be performed together with teams at the Helmholtz-Zentrum Hereon that focus on ocean turbulence and machine learning as a part of the TRR181 Collaborative Research Centre
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challenging, innovative, and dynamic research projects. They will learn the routine lab techniques and build strong analytical skills. The work between the two labs is at the interface of biology and
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of the detector upgrade. The LPC is also a major hub for Machine Learning developments for particle physics. There is close and frequent collaboration with the Fermilab theory community. The LPC provides
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educator resources, paying particular focus to the development of enterprise skills in the computing and software development context. Expected outcomes include stronger industry-academia collaboration
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team of over 15 full-time researchers offers a stimulating and supportive environment to learn and grow. Your profile Required qualifications: Undergraduate degree in Civil Engineering or a related topic
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of measles evolution and naming systems (nomenclature) to support public health identification of measles outbreaks/importation and ultimately, elimination. In this project you will learn to perform genomic