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Field
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intersection of machine learning and life sciences, developing next-generation models that improve our understanding of human biology and enable more proactive, personalized healthcare. As an Industrial PhD
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programming skills in Python; initial experience with machine learning frameworks such as PyTorch or TensorFlow Initial practical experience from a master's thesis, study projects, internships, or open-source
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Beginning Winter semester Application deadline All students – online application: 1 March for the following winter semester https://www.lmu.de/psy/de/studium/doctoral-training-program-in-the-learning-sciences
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allow for more distributed and variable generation. Job requirements Essentials: Basic knowledge of power systems and machine learning/AI. Completed an MSc degree in a highly technical related discipline
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missing and incomplete symptom data can introduce bias and worsen existing inequalities. This PhD will develop innovative statistical and machine learning approaches to understand, model, and overcome
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team includes: Prof. Ivan Depina – main supervisor and coordinator, probabilistic modelling, scientific machine learning Prof. Mohamed Hamdy – building performance simulation, building automation
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-NorthWind webpage for more details): · Hybrid Multiscale Modelling · Hybrid Physics-Machine Learning Analyses · Coupled Atmosphere-Turbine Models · Uncertainty Quantification in Hybrid Frameworks We offer
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): · Hybrid Multiscale Modelling · Hybrid Physics-Machine Learning Analyses · Coupled Atmosphere-Turbine Models · Uncertainty Quantification in Hybrid Frameworks We offer the opportunity to work in a very
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience, sustainability, and environmental