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part of a team, able to learn quickly, meet deadlines and demonstrate problem solving skills. Thorough knowledge of web, application and data security concepts and methods. Preferred Qualifications PhD
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 18 hours 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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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position
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to capture the spatial complexity of tumor organization and its relationship to treatment response. This PhD project aims to develop robust multimodal predictive models of platinum resistance using a large
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vision and novel applications of machine learning. Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is preferred. Apply online at https
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. Additional duties may include contributing to writing grant proposals; working with large datasets; analyzing data for accuracy and data integrity; developing questionnaires for follow-up studies
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teaching interests that would further strengthen an application include machine learning, computational economics, or data-intensive methods, particularly where these areas complement macroeconomic analysis
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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a