Undergraduate students strive to complement their studies with research internships. For this, they send hundreds of emails, hoping to land an opportunity. Similarly, professors who offer research positions receive hundreds of emails with such inquiries, making it an inefficient process in their busy professional life. Moreover, online job-search platforms, such as Indeed or LinkedIn, do a poor job when it comes to research internships especially for undergrads as they are based on job postings. We propose to create an online platform to connect undergraduate students to research internships in academia. Our system will not depend on job postings – contrary to the matching paradigm employed by all job-search tools. Instead, we determine the research topic a lab focuses on, and hence their project topics, through web crawling, data scraping, machine learning, and natural language processing algorithms. This will minimize the need for a professor to constantly maintain and update their profile. We believe this is a better suited job-search paradigm for the academic setting, and especially for undergrads. Our system will save time both for the students and professors. With our intelligent matching algorithm, we also believe that our system increases the chance of finding the correct student and professor.
