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Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains
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. You will be part of our team and develop new ideas, technologies and experiments to provide new insights on macroscopic quantum physics, on gravity at small scales and, in the long run
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Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods
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for development: Success in life depends on what you make of it, but if you are ambitious and successful, there are plenty of opportunities to connect you to all relevant top research groups in the world in quantum
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research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from chem- and bioinformatics to computer vision and
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as evidence of ongoing CIN. This PhD will work at the leading edge of single-cell transcriptomics and modern statistical/AI methodology to address this gap using in-house developed cell atlases. We
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-cell transcriptomics and modern statistical/AI methodology to address this gap using in-house developed cell atlases. We will develop and benchmark approaches that infer copy-number changes and CIN
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? Such questions are the driving force behind our research. You will be part of our team and develop new ideas, technologies and experiments. Your personal sphere of influence: As university assistant (PraeDoc
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publication-based PhD qualification, including: - Review of previous literature and the relevant theoretical background - Analysis of existing/secondary survey dataset(s) - Development of new survey and
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dataset(s) - Development of new survey and collection of original survey data - Development and implementation of experimental studies - Writing and contributing to scientific papers for peer