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neurotechnologies. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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of patients. The candidate will contribute broadly to the project to enable the development of new algorithms for clinical AI based on patient data from heterogeneous sources, notably language/speech-based
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to submit a professional development plan no later than one month after commencement of the postdoctoral period. It is expected that the successful candidate will be able to complete the project in
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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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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
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innovative material solutions. This project tackles this challenge head-on. The project vision is to develop a pioneering AI-driven methodology for designing Functionally Graded Materials (FGMs) specifically
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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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. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including