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setting, with impactful applications in medical research. The project will suit students with a strong background in an appropriate quantitative subject such as mathematics, statistics, machine learning
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the laboratory. Modeling and simulation skills (batteries, energy systems, electric equivalent circuits). Machine learning, statistical analysis, and other contemporary data-driven techniques. Computational
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for perceptual and creative relevance; Curate and/or utilize benchmark datasets of pareidolic visuals, and apply statistical and machine learning methods to analyze visual data and model behavior; Publish and
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regulation of digital markets and platforms. We are seeking applicants with a robust quantitative background (e.g. analytical modeling, econometrics and/or machine learning). Candidates who hold an MSc, MPhil
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validate the identified key (epi)genetic molecular and explore their therapeutic potential in vitro and in vivo disease models. Profile The candidate should: have an MSc in Systems Biology, Molecular Biology
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explores quantitative techniques that identify fake news within the climate change domain. The techniques are based on statistical and computational approaches, including machine learning algorithms
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PhD Studentship: Discovering Causal Determinants of Positive Child Development in Observational Data
for our PhD programme, this project requires strong conceptual thinking, excellent statistical skills and the ability to acquire new statistical expertise when needed, advanced writing skills, and the
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Website https://www.academictransfer.com/en/jobs/350125/phd-candidate-in-causal-learnin… Requirements Specific Requirements You are/ You have: a MSc degree in statistics, machine learning, data science or a
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of differential and probabilistic programming. This exciting new field of research combines knowledge from the machine learning, programming languages, scientific computing, and statistical physics communities
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of differential and probabilistic programming. This exciting new field of research combines knowledge from the machine learning, programming languages, scientific computing, and statistical physics communities