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and well-structured PhD trajectory, the PhD fellow will be required to develop a career and competence development plan in dialogue with the supervisory team early in the appointment period. Aksel
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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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hairy 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
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admission criteria see https://www.nmbu.no/en/research/regulations-and-guidelines-doctoral-degrees-nmbu To be employed, you cannot have previously held a PhD position at NIBIO or with funding from
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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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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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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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and possible exemptions can be found here: https://www.sv.uio.no/english/research/phd/structure/programme-description.html#two Desired qualifications Experience with statistical genetics, polygenic
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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