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of environmental stressors to individuals throughout their lifespan. It is estimated that 70% of all chronic disease burden are determined by exposome, with only 30% are dependent on individual’s genetic makeup
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on the topic of multistage stochastic optimization problems with integer decision variables. The goal of the PhD project is to develop and implement new exact and approximate solution methods for these highly
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of qubit arrays, the study of novel control methods for high-fidelity operations, the shuttling of electron spins across the chip, and the use of superconducting resonators for fast spin readout and for
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respect to drought and Nitrogen-deficiency. Next, the candidate will construct a predictive hybrid model by (a) retrieving estimates for performance and resilience-defining parameters from experimental time
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modelling, and an ability to learn new concepts and (computational) methods as needed Affinity with scientific computer programming (e.g., R, Matlab, Python) Proficient communication skills in spoken and
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Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about the history of the built environment, urban health and digital research methods? Join TU
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Specific Requirements - We are searching for an excellent and motivated candidate holding a recent master’s degree in Applied Mathematics, Computer/Data Science, Physics or equivalent. - A solid background
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position within a Research Infrastructure? No Offer Description Our research focuses on computational imaging for biomedical applications. By integrating advanced numerical methods with customized optical
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position within a Research Infrastructure? No Offer Description Our research focuses on computational imaging for biomedical applications. By integrating advanced numerical methods with customized optical
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operation and track its health, recurrent neural networks, particle and/or extended Kalman filters, and inverse parameter estimation methods might be worth exploring. By uncovering the dependency between