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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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.0c02887 ). We are looking for a motivated student with keen interest towards experimental research and applications of trace gas analysis. An optimal candidate should have prior experience with optical
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development. The successful candidate will contribute to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model
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, the appointee’s work will cover some of the following areas: Development of an isotope version of the process-based CH4 model and parameter optimization for different wetland types. Coupling the updated CH4 model
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the determinants of optimal policy. The research topics of FIT include various themes such as: measuring the extent and determinants of income and wealth inequality; the role of taxation and related regulation in
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experimental (wet-lab) approaches to tackle challenges such as: Prioritizing drug combinations based on single-drug response data Optimizing treatment strategies for synergy, efficacy, and toxicity Deciphering
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fully automated Prompt calibration which can save tens of person years, millions of CPU hours and speed up the typical CMS analysis cycle times by several years. In addition, it allows better optimization
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necessary for the position. In the postdoctoral researcher's position, the degree requirement (doctoral degree) must be met by the beginning of the employment. The optimal candidates will have acquired a PhD