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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a highly motivated PhD candidate to advance predictive, data-driven production and
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English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade
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computational models generate hypotheses and, with the help of partner labs, validate them in controlled systems. The end goal is a mechanistic and clinically relevant map of how CIN shapes cancer behavior and
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, AtomGPT). Working Knowledge Of: • Workflow tools (e.g., ASE) and HPC environments. • Software development in Python, Git-based version control, and Conda packaging. • Data integration and surrogate modeling
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applied in particular to the modeling of 3D-printed concrete at the Navier laboratory, to better predict complex phenomena such as material curing and crack formation. Where to apply E-mail jeremy.bleyer
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Postdoctoral Positions in PFAS Analytics, Degradation, and Thermophysical Properties - DTU Chemistry
thermophysical properties vary across the diverse PFAS chemical space and how these properties may be predicted using computational models. These positions offer an excellent opportunity for early‑career
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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
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, based on detailed studies of Earth and the solar system, is developing predictive models to identify habitable planets around other stars. Within three different research themes: (1) Planets and Early
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reports to develop computational models that predict identification reliability. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast