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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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-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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suitable host organisms for recombinant production identify and solve metabolic bottlenecks develop cost-effective multi enzyme cascades with cofactor regeneration (if applicable) for in vivo and in vitro
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the way of living as a scientist with experimental focus? Do you want to contribute to pushing the boundaries of scientific knowledge? Are you a team player who loves to discuss science and to develop
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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
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, with possibility of one extra year extension. ROLE WITHIN THE RESEARCH PROJECT Microscopy of fluorophore tagged transcription factor localization upon different stimuli Development and optimization
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. Candidates should have expertise and interest in one or more of the following areas: Design and execution of user studies addressing visualization research questions Development and maintenance
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with other partners in the consortium develop a life-cycle-analysis (LCA) framework for selected pigment manufacturing and dye application processes, effectively incorporating process engineering data