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knowledge and understanding of scientific theory, in particular social science and statistical methods, when precedents do not provide specific guidance. General instruction provided by the supervisor as
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scientific computing, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear
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programs. Alternatively, Mathematics, Computer Science, Computer Engineering, Electrical Engineering, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent
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a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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graph theory. Qualifications Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are welcome to apply
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managing and analyzing data. Strong analytical thinking and problem solving skills. Req Expertise in statistics, algorithms, graph-based software. Req In-depth knowledge of the principles and practices
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, such as software testing and runtime verification. For further information, you may refer to https://www.uni.lu/snt-en/research-groups/svv Main responsabilities: Tool Extension & Integration: Develop a
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, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear algebra, machine
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-based networks graph-based approaches Bayesian learning information theory Documented strong programming skills (preferably Python), for example with contributions to open-source projects, with an active