26 algorithms-"DIFFER"-"NTNU---Norwegian-University-of-Science-and-Technology" positions at Nature Careers
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. This research project has a dual focus. On the one hand, you will be involved in analysis of spatial, single-cell and multi-omics data to efficiently characterize the different molecular layers. This will be done
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
currently relies solely on the surgeon’s expertise [2]. Unlike scheduled orthopedic procedures, trauma surgery has seen little integration of artificial intelligence in preoperative planning. Currently
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and
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employment This is a 7-month position from October 1st, 2025, or as soon as possible. Job description You will participate in the ongoing development of an algorithm platform for digital shadows and twins
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of the canopy and longer wavelengths being most sensitive to the characteristics of trunks and soils. Recent research has shown that phase difference between two temporally separated SAR acquisitions is also
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mesh complexity. Most greedy algorithms utilize local operators [2, 1], or variational approaches [5] or different stages (topology, then geometry) [6], or a larger repertoire of operators [9]. More
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processes within the soil–plant–atmosphere continuum. Sensing technologies, laboratory, field, and regional-scale experiments, as well as modeling approaches are used to bridge gaps between the different
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, algorithm implementation, programming from a variety of biotechnology platforms, and oversee quality check. Design and prepare materials and courses for training on various bioinformatics software and
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differences or random direction stochastic approximations, offer general-purpose strategies that only rely on function evaluations. While conceptually simple, zeroth-order methods tend to scale poorly with