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subsurface leaching into groundwater within agricultural systems experience in statistical analysis of research results or willingness to acquire such as well as to complete a PhD degree self-motivated
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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interdisciplinary desire to learn and willingness to cooperate, openness for internationalization and diversity, very good verbal and written English communication skills as well as the absolute determination
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of Physics by means of various spectroscopy schemes and also at Q.ANT in their gyroscope/magnetometer labs. With this thesis, the PhD student will acquire broad knowledge on state-of-the-art laser technology
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, remanufacturing, repurposing and recycling to each other for the realization of an agile network. Various machine learning approaches will be used here. Your tasks are: Requirements definition, survey and
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, workshops and e-learning opportunities per year for personal development, as well as a wide range of health and sports activities Remuneration according to TV-H, company pension scheme, child allowance and
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looking for PhD Students in areas related to: Cybersecurity, Privacy and Cryptography Machine Learning and Data Science Efficient Algorithms and Foundations of Theoretical Computer Science Software
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looking for PhD Students in areas related to: Cybersecurity, Privacy and Cryptography Machine Learning and Data Science Efficient Algorithms and Foundations of Theoretical Computer Science Software
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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are looking for first-class graduates with expertise in the CRC-addressed PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, openness for internationalization and diversity, very