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period at the University of Oslo. Place of work is Department of Informatics at Blindern, Oslo. Job description Unsupervised machine learning (ML) methods are widely used to explore structure in complex
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fundamental insight into the structure-composition-function correlations that govern the performance of heterogeneous catalysts in reactions relevant to the Cyclic Carbon Economy: CO2 hydrogenation with green
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of the Center for integrative neuroplasticity (CINPLA) and in the INTED center. This PhD project will focus on reinforcement learning methods for generating complex structures with two possible application areas
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surfaces and when actively driving a soft sheet near a wall. Essential to the projects is developing a new understanding of the fluid-structure interactions, that is to say, the coupling between hair’s
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machine learning (ML) methods are widely used to explore structure in complex and high-dimensional data, particularly in the life sciences, where clustering analyses often form the basis for biological
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the neuroscience work package which will investigate how HC use during adolescence influences structural and functional brain development and depression risk. Adolescence is a critical period of brain maturation and
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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 requirements: The norm is as follows
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communication skills in 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
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. The project will collaborate closely with, and utilize existing linked data from, Regforsk at NTNU (https://www.ntnu.no/ism/forskning/regforsk#/view/publications). Deakin University in Melbourne, Australia is
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project will focus on reinforcement learning methods for generating complex structures with two possible application areas (i) the generation of virus capsids for gene therapy and (ii) the generation