Bann Seng Tan
I hold a joint appointment with the Department of Political Science and the Department of International Relations. This means some of my offered courses may be cross-listed with both departments.
Teaching Assistants (TA) & Teaching Fellow (TF) Positions.
Every semester, depending on enrollment, I look for either TAs and/or TFs.
TAs positions are for Ashoka students. Applicants have to apply through the IR/PS departments.
TF applicants should be at the master's level and have some background in Political Science. TFs are paid and Ashoka University rules apply.
Interested applicants should send a i) CV, ii) a transcript, and iii) highlight the courses in Political Science/International Relations/Methods classes and the grade earned for them to my official email at Bannseng.firstname.lastname@example.org. I will contact the shortlisted for interviews.
Current status of application round for Fall 2022: Not looking.
About Letter of Recommendations
From time to time, I get requests for letters of recommendation for graduate school. Drafting such letters take time and there are professional norms involved that you may not know but should be aware of. One cardinal rule is that I do not entertain last-minute letter requests.
When you email me for such requests, I typically respond with a set of instructions on the information I require from you. You can read about them here. You have to do the work and provide that information. Put another way, an effective letter of recommendation is a costly signal. It takes effort from both of us. It is best you seek it from someone who has had extensive experience working with you in a professional setting. Just attending the professor’s classes alone may not be enough. A good way to build professional relationships is to provide research assistance to the professor.
On that note, I am interested, on an ad-hoc basis, in students can provide research assistance in:
1) the politics of foreign aid and “donor switching” by aid recipients; or
2) the politics of natural disasters; or
3) web scraping/text analysis of traditional news media.