80 cyber-physical-system-data-mining PhD positions at University of Groningen in Netherlands
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Organisation Job description We are seeking a candidate for a PhD project on the philosophical aspects of data science and data science use, with specific attention for the use of data science
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at uncovering how the capacity for Darwinian evolution may have first arisen. While parts of this puzzle have been solved, an important open question is the emergence of information transfer (inheritance), one
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PhD position - Modelling the emergence of information transfer in prebiotic self-replicating systems
how the capacity for Darwinian evolution may have first arisen. While parts of this puzzle have been solved, an important open question is the emergence of information transfer (inheritance), one
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Organisation Job description Project and job description Our project will make use sensing technologies (hyperspectral cameras, NIR and Raman sensors), and an edge-compute AI pipeline to sort used
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is a science and creative hub that utilizes bird-borne tracking technology to generate and analyze vast datasets related to ecological change. By combining data from transmitters, loggers, and other
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results, further research is guided by trial and error with the goal of deriving intuitive trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular
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. Important themes are logistical organisation of regional care and prediction of treatment outcomes for individual patients. Research activities involve collecting (prognostic and care logistics) data
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Organisation Job description A fully funded PhD position in the broad field of systems and control is available at the University of Groningen, the Netherlands. The specific subject of the project
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experience with data analysis (e.g., R, SPSS, Stata, Python, SQL), web scraping, and data management. Familiarity with open science practices (e.g., GitHub, database management) is a plus. You are eager
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create