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Field
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scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on
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in machine learning or deep learning (experience with deep learning frameworks such PyTorch and TensorFlow is a plus). You have solid programming skills in Python. You have programming experience in C
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tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/phd-position-computer-simulati… Requirements Research FieldEngineeringYears of Research Experience1 - 4 Research
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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets. What we offer: • A stimulating
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related subject/area) and have technical skills in computer programming and basic knowledge of mathematical models for systems and/or synthetic biology. LanguagesENGLISHLevelGood Additional Information
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead of time-consuming simulations to evaluate scenarios for improving the user experience along cycling and
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an open working model that combines remote working with work on-site, according to organizational needs and the nature of the tasks involved. About the UOC A leader in e-learning, our pioneering