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and structural effects of various peptides on both simplified and complex membrane systems. The project will entail a combination of computational and experimental work, both directly and in
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), (c) estimation methods for latent variable models (e.g., two-step approaches or approximate maximum likelihood estimation), or (d) meta-analytic models to address complex data structures (e.g., spatial
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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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Proficiency in written and oral English Good knowledge of Norwegian language is an advantage Demonstrated ability to work independently in a structured manner, while also possessing strong collaborative skills
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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 The Research Council
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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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conditions is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills. Applicants must have good written and oral English skills
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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