14 algorithm-development-"the"-"The-Netherlands-Cancer-Institute" positions at University of Bergen in Norway
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thanotyping (5-D)”, financed by the European Research Council (ERC). The 5-D project will develop methods and digital tools to identify that a person with dementia is at the end of life, aimed to understand
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project ”Decoding Death and Dying in People with Dementia by Digital thanotyping (5-D)”, financed by the European Research Council (ERC). The 5-D project will develop methods and digital tools to identify
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for basic research. The successful applicant will contribute to the further development of the Coincidence Analysis (CNA) method for causal data analysis. Currently, CNA has four key limitations: (I
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project bridges two foundational fields in computer science and mathematics: Theory of Algorithms and Extremal Combinatorics. By integrating these areas, the project seeks to develop innovative
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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collaborative skills. Applicants must be proficient in both written and oral English. Experience from one or several of the following areas is an advantage: Developing algorithms for CFD solvers (e.g. OpenFOAM
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advantage: Developing algorithms for CFD solvers (e.g. OpenFOAM). Programming in C++ or Fortran and proficiency with MATLAB or Python scripting. Experience with tools for simulating chemical kinetic, e.g
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components