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About this position
This announcement is for a fully-funded Research Fellow position in the time complexity analysis of bio-inspired computation at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), located in Shenzhen, China.
The Research Fellow will contribute to understanding the mathematical foundations of bio-inspired optimization heuristics, which include algorithms like evolutionary algorithms, genetic algorithms, and artificial immune systems. These are meta-heuristic techniques inspired by biological processes and widely used in AI for optimization problems.
Key responsibilities include:
Developing mathematical methods to analyze the time complexity of these algorithms.
Investigating the impact of different algorithmic parameters on their performance.
Conducting computational experiments.
Disseminating findings through conferences, journals, and collaborations.
The successful candidate will be part of the AI-Theory Lab, led by Professor Pietro S. Oliveto, which focuses on the theoretical analysis of bio-inspired computation techniques.
Required Skills
A PhD in Computer Science (or close to completion) or a related field.
Expertise in bio-inspired computation, time complexity analysis, and computational complexity. Strong programming skills in Java and C.
Experience with LaTeX, SVN, GIT, or equivalent tools.
Project Focus
The project will explore various key aspects of bio-inspired algorithms:
Population-based search heuristics: examining their efficiency compared to single-trajectory algorithms.
Algorithm configurators: evolving optimal parameters.
Hyper-heuristics: evolving the heuristic itself.
Genetic programming: enhancing the effectiveness of evolving computer programs.
Salary and Benefits
The annual salary is 332,550-450,000 RMB, with additional meal and festival allowances. The position also offers funding for conferences and research collaborations globally.
How to apply
For further information, refer to Professor Pietro S. Oliveto’s personal webpage.
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