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Field
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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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over Morocco. Key Responsibilities: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation and temperature in
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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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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research
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computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning models for prediction and recommendation Job
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operation of the umFm module unit used in vertical farm which includes handling of soil compost, irrigating of the crops and taking note of the sensors incorporation in the vertical farm inhouse. Job
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new in situ temperature observations Moana sensors deployed on fishing gear (to 0-1000m depth) in conjunction with other extant observations where available (e.g glider, drifter, Argo, satellite remote
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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) The University of Strathclyde has a significant depth of space research expertise across its Applied Space Technology Laboratory, the Sensor Signal Processing & Security Laboratory, and the Aerospace Centre
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: The successful applicant will be responsible for: Obtaining rigorous mathematical results at the interface of optimization, geometry, and data science Designing, implementing, and testing algorithms Engaging in