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36-hour week. Lots of options when it comes to secondary employment conditions; we can, for example, discuss options for a sabbatical or paid parental leave. Within our terms of employment individual
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and have access to state-of-the-art industry experts, data and knowledge, allowing you to make an impact during your PhD research. In this position you will work with real world data and models, aimed
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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to train an AI model that predicts the cis-regulatory code for synthetic genomes (i.e. for cell-free gene expression systems) and correlates the experimental conditions within the synthetic cell
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Sciences, Pedagogics or a related discipline. You have strong analytical skills and experience with complex quantitative analytical methods (e.g. multilevel and/or structural equation models). You are
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diseases, and how these influence, or are influenced by, labor force participation and income. In addition, you will develop simulation models to predict how different policies could reduce the disease
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cycles to continuously improve models via active learning and guide evolutionary trajectories toward promising but otherwise inaccessible sequence spaces. You will be embedded in one of the three research
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, analysis, and model choice while retaining strong error guarantees. This means that researchers can adapt their research questions and sampling plans to the data as they come in and in a way that is as model
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work towards building thorough and encompassing theoretical models that present relevant empirical implications. This PhD project offers a unique opportunity to work in an international environment in
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conditions pose challenges to ensuring the safe quality of AM parts and affect their strength and consistency. The main objective of this PhD position is to address these issues by developing an advanced