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Field
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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sound statistical models and methodology for the data-driven analysis of complex and dynamic systems, with the goal of enabling predictive, prescriptive, and pre‑emptive analytics. We work closely with
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multi-data analytics (e.g., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis
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rare diseases. Experience with molecular biology is highly desirable. The ideal candidate demonstrates: A critical and analytical scientific mindset Attention to detail and a structured approach to data
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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Institut de Recherche pour le Développement (IRD) | Sete, Languedoc Roussillon | France | 4 days ago
the island food systems of the Comoros archipelago (incl. Mayotte) in order to develop an analytical framework and a model for assessing the vulnerability and resilience of these systems to climate risk. Under
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analytical skills and have a problem-solving, result-oriented attitude and can communicate clearly. - You are proficient at presenting to effectively communicate complex results. - You are a communicative and
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio