530 machine-learning "https:" "https:" "https:" "https:" "The Open University" PhD positions in Germany
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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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research areas. New insights and synergetic effects resulting from collaboration between inherently different viewpoints of separate fields typically accompany this endeavour. Our task force on machine
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Structured doctoral training is generally provided by the GGL graduate centre or graduate schools. The objective is for doctoral students to acquire and deepen their knowledge, broaden their scientific
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 8 hours ago
researchers. The doctoral research topic, which is assigned to one of the chairs offered , also allows doctoral students to contribute their own institutional experience. A structured and international learning
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-academic training such as skills courses. A full description of our PhD training is available on our website: https://imprs-cpqm.mpg.de/105837/PhD-training . A Diploma supplement will be issued Yes
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of visualisation, machine learning, and human-computer interaction under the joint supervision of both institutions. The position is shared by TU Wien and USTP and offers the opportunity to conduct research at both
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or TOEFL Submit application to https://www.grk2338.med.uni-muenchen.de Accommodation The International Office helps visiting academics, PhD students, and postdocs who are travelling to Munich for a set
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development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS): https://go.fzj.de/JuDocs FAIR
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data