A rise of academic capitalism over the past four decades has been well documented within many research-intensive universities. Largely missing, however, are in-depth studies of how particularly situated academic groups manage the uncertainties that come with intermittent and fickle commercial funding streams in their daily research practice and problem choice. To capture the strategies scientists adopt under these conditions, this article provides an ethnographically detailed (and true) story about how a single project in Artificial Intelligence grew over several years from (...) a peripheral idea to the very center of an academic lab’s commercial portfolio. The analysis theorizes an epistemic form—nimble knowledge production—and documents three of its lab-level features: 1) rapid prototyping to keep sunk costs low, 2) shared search for “real world problems” rather than “theoretical” ones, and 3) nimble commitment to research problem choice. While similar forms of academic knowledge transfer have been lauded as “mode 2,” “innovative,” or “hybrid” for initiating cross-institutional collaboration and pushing science beyond disciplinary silos, this case suggests it can rely on fleeting attention to problems resistant to a quick fix. (shrink)

Changes in knowledge production, increasing interaction between government, universities and industry, and changes in labor markets for doctoral degree holders are forces that have spurred a debate about the organization of doctoral education and the competencies graduates need to master to work as scientists and researchers in a triple helix research context. Recent policy also has supported a redefinition of researcher training with increasing focus on broader skills and relevance for careers outside the university sector. Consequently, it is pertinent to (...) investigate current changes in doctoral education and researcher training. Particularly further knowledge about university–industry collaboration as a context for researcher training is required. With this in mind, this study provides empirical illustrations of how research training carried out in collaborative research contexts is experienced by doctoral students, and offers some insight into antecedent and process factors that are central in shaping PhD students’ research and training experience in collaborative research contexts. Based on the empirical data and a review of existing literature, suggestions for further research are made. (shrink)