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Field
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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation
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Infrastructures Didactics of Informatics Digital Humanities Distributed Systems High-Performance Storage Machine Learning Medical Informatics Neural Data Science Practical Informatics Scientific Information
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datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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data. Develop and apply machine learning models to estimate uncertainty in climate impact statements. Analyse spatial and temporal patterns and trends in climate-extreme impacts. Cross-validate
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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programming task within the thematic context of the advertised position: https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bi-molecular-machine-learning/ Employment conditions This is a
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international research environment covering a wide variety of research areas, such as algorithms and data structures, machine learning, computer graphics and vision, database systems, artificial intelligence
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check our website for more information: https://www.uni-goettingen.de/de/635183.html PhD students with their own funding (e.g. DAAD) can join at any time. Tuition fees per semester in EUR None Combined
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applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language skills. Experience with