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- $83,468 - $113,262 p.a. plus 17% super Contribute to innovative research in algebraic graph theory. Work with world-class mathematicians. Investing in you - benefits package including salary packaging
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on the boundary of model theory, group theory and geometry to develop new insights about definable groups, Diophantine problems (around Pila-Wilkie) and graph-combinatorial conjectures (such as
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Condensed Matter Physics and Materials Sciences o Theoretical and Computational Biophysics o Soft Matter Physics o Physical Chemistry and Theoretical Chemistry o Combinatorics, Algorithm, Extremal Graph
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, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory o Theory and Computation
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through innovative methods, theory and critical analysis. The fellowship period is 3 years. Starting date as soon as possible and upon individual agreement. An extension of the appointment by up to twelve
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, collaborating across disciplines to tackle fundamental challenges through innovative methods, theory and critical analysis. The fellowship period is 3 years. Starting date as soon as possible and upon individual
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) Singapore Assistant/Associate Professor in Information Systems and Technology Kuwait (KW) Research Fellow in Algebraic Graph Theory Melbourne, Victoria, Australia
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for excellent scientists with background and experience in one or more of the following areas: graph algorithms, parameterized complexity, approximation algorithms, extremal combinatorics, structural graph theory
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to implement and optimize AI/ML models for biomedical datasets. Preferred Knowledge, Skills and Abilities Mathematical Modeling: Strong foundation in numerical modeling, graph theory, and statistics. Algorithm
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4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
, e.g. self-supervised learning, convolutional neural networks, transformer-based networks, eigenvalue/eigenvector-based methods, graph-based approaches, Bayesian learning, information theory, geometric