Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
-learning acceleration at the Department of Computer Science, Norwegian University of Science and Technology (NTNU). The position is supported by the EU Horizon project CIRES and offers the opportunity
-
natural particle accelerators on Earth, capable of accelerating particles to relativistic energies and producing gamma-rays on different time scales, from sub-millisecond to tens of seconds and more
-
under the Green Platform programme, which is jointly supported by the Research Council of Norway, Innovation Norway, and SIVA. The programme aims to accelerate the green transition in Norwegian industry
-
collaborations between the Department of Biosciences, the Natural History Museum, the Department of Mathematics, and the Department of Philosophy. To respond effectively to the accelerating decline in biodiversity
-
of Philosophy, Classics, History of Art and Ideas. To respond effectively to the accelerating decline in biodiversity, society urgently needs decision-making systems that are scientifically robust, transparent
-
for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant response behavior (such as rapid guessing, cheating, or careless responding
-
samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive
-
produced embryos and embryo transfer are useful in breeding as tools to accelerate genetic gain. This project will characterize factors related to in vitro maturation of oocytes and study key proteins and
-
computing on high-performance supercomputers such as Saga and LUMI to accelerate drug candidate identification. The position may include secondments to industry partners for biophysical analyses (X-ray
-
effectively to the accelerating decline in biodiversity, BioM will unite ecology, statistics, and philosophy to improve the modelling and governance of biodiversity under uncertainty. The project develops