CRA Bulletin

The CRA Bulletin frequently shares news, timely information about CRA initiatives, and items of interest to the general community.
Join the RSS feed to stay connected.


Analysis of Current and Future Computer Science Needs via Advertised Faculty Searches for 2019


By Craig E. Wills, Professor and Department Head, Computer Science Department, Worcester Polytechnic Institute

This work uses the same methodology as work over the past five years to study where Computer Science departments are choosing to invest faculty positions by examining data obtained from advertised faculty searches for the current hiring season.  While the number of and areas for faculty searches does not necessarily translate into the same for faculty hires, we believe that they provide insight into current and future needs within the discipline.

We analyzed ads from 409 institutions seeking to fill hundreds of tenure-track faculty positions in Computer Science.  There was a small one-year increase in the number of institutions searching but there has been a 83% increase over the five years of our studies.  The number of tenure-track positions sought shows a one-year increase of 5% and a 118% increase over the five years.

We clustered the specific Computer Science topics mentioned in ads into 16 areas.  As part of our work this year we compared this classification with other classifications from the CRA, csrankings.org, csmetrics.org and arXiv.org, which resulted in small adjustments to our classification.

In terms of specific areas, we found that the clustered areas of Security, AI/Data Mining/Machine Learning and Data Science are the areas of greatest investment.  Aggregating the Data Science, AI/DM/ML and Databases clusters again resulted in close to one-third of all hires sought in these data-oriented areas.  We found that roughly 25-60% of all hires are for areas that are, or may be, interdisciplinary in nature.

Differences are also seen when analyzing results based on the type of institution.  Positions related to Security have the highest percentages for all but top-100 institutions.  The area of Artificial Intelligence/Data Mining/Machine Learning is of most interest for top-100 PhD institutions. Roughly 35% of positions for PhD institutions are in data-oriented areas. The results show a strong interest in data-oriented areas by public PhD and private PhD, MS, and BS institutions while public MS and BS institutions are most interested in Security.

The full report containing a description of the methodology and the complete results is available at http://www.cs.wpi.edu/~cew/papers/CSareas19.pdf.

longcluster