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expected to: Investigate how logical and philosophical theories can support the structuring and modelling of knowledge in practical contexts. Participate in the development of IMF by bringing in methods from
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modelling, and artificial intelligence. The postdoc will contribute to both the theoretical and applied dimensions of the IMF initiative and is expected to: Investigate how logical and philosophical theories
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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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, 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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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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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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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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degree is awarded within 4 weeks after the application deadline. A solid background in one or more of the following fields are required: Decision theory or statistics AI and machine-learning techniques
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. The projects may also include to tackle benchmarking problems such as SAT, image processing, graph theories, boson/fermion sampling by applying classical machine/deep learning, neural network techniques and