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Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with industry partner to tackle challenges of practical
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(target journals: International Journal for Numerical Methods in Engineering – IJNME). Deep learning algorithms for high-temperature multiphase problems (target journals: Computer Methods in Applied
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Novel routes for in-situ measurements during the manufacture of thin flexible electronic films. School of Chemical, Materials and Biological Engineering PhD Research Project Self Funded Prof
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and numerical models as well as constitutive model calibration and validation based on physical experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence
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(BMDS Lab) at the Department of Health Sciences and Technology, ETH Zurich, is seeking a highly motivated PhD candidate to join our interdisciplinary research team working at the intersection of data
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. Eligibility criteria We are looking for a highly motivated PhD candidate who meets the following profile: Candidates must hold a Master’s degree in electrical or computer engineering (or a closely related field
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computational engineering, mathematics, computer science, physics, engineering or a related field Strong background in numerical methods and machine learning Proficiency in at least one programming language
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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers
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dissemination, and translational opportunities Job Requirements: PhD in Chemistry with a focus on computational/peptide/organic/machine learning or a closely related discipline At least one first-author