56 machine-learning "https:" "https:" "https:" "https:" "https:" "Helmholtz Zentrum Geesthacht" Fellowship scholarships in Norway
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30th April 2026 Languages English Norsk Bokmål English English PhD Fellow in Machine Learning Apply for this job See advertisement About us The Nansen Center is a Norwegian environmental research
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and accelerate the development of more high-performing PNSEs. The ultimate goal of the project is to develop, implement, and validate novel deep-learning models for molecular dynamics and coarse-grained
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conditions and tailoring nutritional requirements to individual embryos. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/297399/phd-fellowship-in-rna-modification-in-early
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associated with research group of Professor Abhik Ghosh (https://en.uit.no/project/softmatter ). The Ghosh group is renowned for its fundamental discoveries on corroles and is currently exploring a variety of
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practical information about working and living in Norway can be found here: https://uit.no/staffmobility Application Please note that the application will only be assessed based on the information submitted
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Integrated Circuits or Automation. Background in computational optics, inverse scattering algorithms, label-free quantitative tomography algorithms, optical simulations, image analysis or machine learning
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inquiries, please contact hrtkd@oslomet.no Deadline for application: May 4th, 2026 Ref: 26/06383 Where to apply Website https://academicpositions.com/ad/oslo-metropolitan-university/2026/phd-fellow-i
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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certification authorities. Knowledge of experimentation and research methodology. Proficiency in quantitative research methods and familiarity with relevant computer programs, such as SPSS, SAS, or STATAl