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23.07.2025 Application deadline: 08.08.2025 The Institute for Theoretical Physics at the University Tübingen is looking to fill a PhD Position in Quantum Information Theory (m/f/d, E13 TV-L, 50
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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
Biosustain) Recent progress in our ability to read and write genomic code, combined with advances in automation, analytics and data science, has fundamentally changed the scope and ambition of harnessing
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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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and implantable sensors o Microfluidic-based electrochemical platforms o Data-driven sensor signal processing Fluent English proficiency (B2/C1 level or higher). Strong analytical, problem-solving
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Natural Resource (ENR) Group at Wageningen University & Research as a PhD candidate and explore the economic trade-offs involved in the transition to renewable energy. As a PhD candidate, you will assess
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Administration NAV. Prognostic factors for long-term effects on health and welfare will also be explored. The PhD candidate will be in charge of cleaning, linking and statistically analysing data from the various
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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
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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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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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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