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on JavaScript in your browser and try again. Ulla Schildt/NHM 10th August 2025 Languages English English English Natural History Museum PhD Research Fellow in Plant Evolutionary Genomics Apply for this job See
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the closing date for applications. The applicant must have good programming skills, excellent knowledge of algorithms, numerical methods, and signal processing Mandatory experience and formal training: signal
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the PhD has been awarded at the latest within 5 months after the closing date for applications. The applicant must have good programming skills, excellent knowledge of algorithms, numerical methods, and
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change, evolutionary events (radiations and extinctions), plate tectonics, and changes in geochemical cycles. The project will have a focus on invertebrate paleontology, with new sampling of sections with
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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-time control & optimization algorithms. Pilot demonstrations on partner radio telescope facilities such as APEX and the Sardinia Radio Telescope may be possible, depending on the interest of the PhD
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such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological
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the renewable energy management system by developing novel real-time control & optimization algorithms. Pilot demonstrations on partner radio telescope facilities such as APEX and the Sardinia Radio Telescope may
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borehole 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
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods