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Field
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highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to predict the impact of mutations on genes in the avian flu virus and the viral host which
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binding pockets. About the role We are seeking a highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to identify domain functional families
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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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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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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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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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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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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 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
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inversion techniques and signal processing. Strong programming skills, Proficiency in scientific computing (e.g. Python, MATLAB, or similar) for algorithm development and data handling. Experience with sensor