研究方向： 1.Topological Data Analysis for Statistical Learning Keywords: Topological data analysis, graph machine learning, time-varying graph analysis, rare event modelling The objective is to develop ground-breaking topological signal processing and statistical learning methods for high-dimensional data. The focus lies on conducting fundamental research to derive scalable methods with robustness and statistical guarantees that form a bridge between existing topological data analysis methods and statistical learning methods with an eye on applications with time-varying data. Various applications including climate data, financial data, genomic data will be considered though the main emphasis will be in methodology development. 2. Stochastic deep learning Keywords: Bayesian machine learning, non-stationary data analysis This project aims to develop a deep learning methodology which is dynamic and statistical at the same time, that is non-deterministic and non-stationary. We will explore various methods from signal processing extending them to nonlinear systems and hence applying to neural networks. 3. Cross domain learning in the brain Keywords: EEG data analysis, computational models for brain learning, audio and visual information processing in the brain This project aims to understand the way information is coded in the brain in different types of stimuli such as audio and vision. What are the common features? How can we utilise this information to compensate for some brain diseases? How can we utilise these ideas in machine learning? 任职要求： 1. Candidates with a doctoral degree or soon-to-be obtained from prestigious universities or research institutes, with outstanding academic performance and good physical health, within three years of graduation, and below the age of 35; 2. Profound theoretical foundation and strong practical skills in related research fields; 3. Strong research capabilities, with excellent English communication skills (reading, writing, listening, speaking); 4. Possess strong independent work and communication skills, with rigorous logical thinking and the ability to solve problems independently; 5. Possess innovative thinking and research abilities; 6. Passionate about scientific research, adhere to academic ethics, have a strong sense of responsibility and initiative, excellent in teamwork, diligent and eager to learn, honest and conscientious.