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Field
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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set
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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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machines Conduct simulations and experimental testing to validate system performance Document and disseminate research results through scientific publications and presentations The PhD candidate will work
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https://academicpositions.com/ad/eth-zurich/2026/phd-positions-in-neurotechnolo… Requirements Research FieldEngineeringYears of Research Experience1 - 4 Research FieldEngineeringYears of Research
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nanocrystals, hybrid perovskites and 2D materials. Development of new data-driven approaches for studies of optoelectronic properties using EM, including machine learning / machine vision algorithms. The balance
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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
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the first, the student will develop expertise on the lab's existing machine perfusion platforms for human and rat liver This will include learning the engineering design, assembly, and operation of
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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
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requirements Essentials: Basic knowledge of power systems and machine learning/AI. Completed an MSc degree in a highly technical related discipline (computer science, electrical engineering, etc.) and you were
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, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the above-mentioned research areas collaboration in national and international research projects, possibly