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Field
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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. Concurrently, you will develop lower order analytical models and perform high fidelity computational simulations to corroborate experimental findings and propose other configurations to be subsequently
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Psychology, Cognitive Sciences, Modeling and Simulation, Computer Science, Measurement and Statistics, or a related field is required. The candidates must have research experience in one or more areas
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Expertise in preclinical mouse models of cardiovascular disease Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job
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Dr Heli Hietala. The postdoc project involves primarily simulations informed by observations, related data analysis and theory/models, comparing various aspects of shock particle acceleration and meso
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assigned to the research project "Randomization of Surrogates for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The
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on various theoretical aspects of molecular magnets, oxide coating materials for the LIGO scientific collaboration, and other quantum materials, and their modeling and simulation. Projects will include
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the project goals. The successful candidate will be working closely with the project PI’s to generate and test hypotheses, develop and run model simulations, and report findings through presentations
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oscillators driven by the storage-ring RF signals to fulfill the function of manipulating X-ray pulses. More specific responsibilities include MEMS design and simulation, testing and characterization