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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
Biosustain) Recent progress in our ability to read and write genomic code, combined with advances in automation, analytics and data science, has fundamentally changed the scope and ambition of harnessing
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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees
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Engineering, Computer Science, Operations Research, or Economics Analytical problem-solvers with excellent logical reasoning and a passion for turning data into decisions Clear communicators in a fast-paced and
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-approved systems. Ensure adherence to data security and compliance protocols. Preferred Qualifications MSc or PhD in Bioinformatics, Computational Biology, Computer Science, or a related field. Strong
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suited to candidates with strong quantitative and analytical skills and a PhD (or near completion) in cancer epidemiology or a related field. Applicants should have experience in applying or developing
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Science, Operations Research, or Economics Analytical problem-solvers with excellent logical reasoning and a passion for turning data into decisions Clear communicators in a fast-paced and highly collaborative
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related discipline. You will have strong experience in one or more of the following areas: electrified powertrains, marine robotics systems, automation, or predictive maintenance using data analytics
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(including email addresses) of two referees; 4. At least one example of written work (in English, including some data analysis) that demonstrates your analytical and writing skills in a social science
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, data analytics, and co-designed interventions. This is an exciting opportunity to contribute to applied research that informs urban policy and planning, particularly around air quality monitoring and
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements