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Associate Professors divide their time equally between research and teaching, and the successful candidate will contribute to the development of research, both individually and in collaboration with others in the department. The main research interests among the present staff in American Studies...
We seek candidates whose research interests enhance and complement the research group in algebraic geometry . The candidate is expected to have research interests related to the theme of the project "Algebraic and topological cycles in complex and tropical geometry''. The main focus areas of...
The position is funded by the RCN project VACSACOD, which aims to understand immune responses in Atlantic cod and is part of the UiO Convergence Environment COMPARE. COMPARE is an interdisciplinary comparative immunology research environment where immunologists, evolutionary biologists,...
Digital Twin (DT) technology can enable significant improvements in the life-cycle management of physical assets. The aim of a DT is to create and maintain a digital replica of an underlying physical system, called the physical twin (PT), to provide insights into the PT’s behavior. The DT can...
The PhD position is a part of the newly formed dScience center (center for Data Science) at the Faculty of Mathematics and Natural Sciences at the University of Oslo and is hosted by the Language Technology Group at the Department of Informatics. The PhD project is titled Inductive bias for more...
The position is located at the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo, and the candidate will work in close collaboration also with the Institute for Cancer Genetics and Informatics (ICGI) at Oslo University Hospital. The...
Deep learning (DL) is entering scientific computing with full force and has the potential to change both scientific computing and society in general. Yet DL has an Achilles heel. Current implementations can be highly unstable, meaning that a certain small perturbation to the input of a trained...
The candidate will develop a machine learning framework for surface flux mapping with drones. The flux mapping uses data assimilation to estimate surface fluxes from atmospheric drone measurements (drone acceleration to infer turbulence, temperature, humidity, greenhouse gas concentrations) with...
This PhD project mainly develops fundamental theories, innovative algorithms and practical tools for distributed machine learning for efficiently supporting data and computation-intensive tasks with applications in smart grid, communications, and renewable energy systems. Federated learning is a...
The main goal of the project is to develop BNNs/GPs for deconvolution of complex gamma-ray data in nuclear physics experiments. Training data will be obtained from simulated data propagated through a full-scale detector simulation. Since robustness and reliable uncertainty estimates are of key...