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Frankfurt School of Finance & Management • | Frankfurt am Main, Hessen | Germany | about 2 hours ago
Combined Master's degree / PhD programme Yes Joint degree / double degree programme Yes Description/content The doctoral programme at Frankfurt School offers rigorous training in Accounting, Economics
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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the unique opportunity to develop skills in analytical, statistical and data science techniques, working with real world data to provide recommendations for UKHSA on how pharmacy OTC data could be used
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, microbiology, environmental science, chemistry, physics or data science. Applications would be keen to blend hands‑on experimentation with advanced analytics to create low‑carbon, nature‑based solutions
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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. The research project of the PhD student will thus focus on aggregating heterogeneous OSINT (Open-Source Intelligence) sources and aggregate retrieved data with cyber-risks indicators of the targeted environment
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Topic: The rapid advancement of digital health technologies presents unprecedented global opportunities and challenges in public health and healthcare, data management and analytics in prevention
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degree in (analytical) chemistry, biochemistry, or a related discipline Hands-on experience in mass spectrometry-based proteomics (e.g. DDA, DIA, PRM) Experience with proteomic data analysis Computer
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning
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candidate with a strong background in computational fluid dynamics (CFD) and specialized expertise in hemodynamics associated with coronary artery disease (CAD). The ideal candidate will hold a PhD in