I am always on the lookout for enthusiastic students (PhD, Honours research, or undergraduate research) with strong qualifications to join my research group. If you have an interest in numerical methods for computational science or data science and have an excellent academic track record, I invite you to see if any of the research topics on this website appeal to you (in particular, have a look at my "Publications by research topic" page, or look at the "Seminars and presentations" page). If you see something that piques your interest, consider to join the team! PhD students:

• Domestic and International PhD candidates normally apply for admission (candidature) and scholarship support by 31 October.  (There is also a second round of applications by 31 May.) If you are interested in applying with me as supervisor, please contact me via email, preferentially before October 10 (May 10). In your email, please include CV, a brief explanation what you are interested in to work on, and scans of transcripts if you have them. (You may also include electronic versions of any thesis or research papers you have written.) If your profile is a good fit for the research group and we can identify a suitable topic, you will be asked to submit a complete formal application which will be due by October 31 (May 31). (PhD start date for the October 31 application round is normally January-March of the next year; start date for the May 31 application round is normally August-December of the same year. Scholarship offers are normally made about six weeks after the application deadlines.) 
• International students who bring their own funding can apply for admission (candidature) at any time of the year (following the same procedure).
• For the October application round, admission results will be communicated to you by mid-December. Successful applicants are expected to start their PhD in January-March of the subsequent year.
  
• Areas that are currently of special interest to me for new PhD projects:
• Distributed algorithms for linear algebra and big data analytics in Spark
• High-order methods for CFD: parallel adaptive cubed-sphere grids (with applications in geophysics)
• Parallel computing in time for large-scale PDE simulation (see also here)
• Models and algorithms for social networks and social media
• Nonlinear preconditioning for nonlinear optimization

Honours research students: