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Field
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. The project will provide the student with extensive training in large-scale data integration, machine-learning methods, field-based environmental monitoring and eDNA analysis, as well as experience working
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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democratization of approaches using artificial intelligence based on Machine Learning (statistical AI), data lakes have also been proposed [4,5]. Objectives: Monitoring farming and agronomical activities is based
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | 1 day ago
information on BTU scholarship opportunities on our websites: https://www.b-tu.de/en/international/international-students/help-advice-on-all-aspects-of-studying/scholarships-1 . Academic admission requirements
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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(1–4) and in related projects. We encourage potential PhD candidates to visit our webpage to learn more about the research we are conducting. The PhD candidate is expected to be enrolled in two
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, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
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, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en/organisation/liu/isy/ks . For more information about working at ISY, please visit: https://liu.se/en
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analysis. • Hydrological and hydraulic simulation. • Machine learning, including unsupervised clustering and predictive modelling. • Working with large, complex, multi-source datasets using MATLAB, Python