Quantitative hedge funds and trading firms are expanding their investment in academic research and dedicated AI laboratories as competition with technology companies for machine-learning specialists intensifies, according to a report by Bloomberg.

Firms including G-Research, Qube Research & Technologies, Capital Fund Management (CFM) and Jump Trading are funding PhD programmes, sponsoring university researchers and establishing specialist laboratories to attract scientists capable of developing increasingly sophisticated trading models.

Competition for talent has driven first-year compensation packages for machine-learning PhDs to between $450,000 and $650,000, with some reaching $800,000, according to recruitment industry estimates. However, quant firms are increasingly competing against AI developers offering researchers greater public recognition and potential equity upside.

Qube Research & Technologies has launched QRT Labs, sponsoring more than 70 PhD and postdoctoral researchers, while G-Research has funded 36 researchers through the European Laboratory for Learning and Intelligent Systems and supports 43 PhD students across eight UK universities.

Paris-based CFM, which manages approximately $35bn, has established a machine-learning laboratory designed to pursue longer-term academic research without the immediate pressure of generating trading signals. The firm views the initiative as a means of identifying advances in AI that could eventually strengthen systematic investment strategies.

Other firms, including Hudson River Trading, XTX Markets, JPMorgan and Optiver, have established dedicated AI research operations, while Jump Trading has expanded its academic partnerships to strengthen recruitment.

Competition is also driving movement between quantitative finance and the technology sector. Hudson River Trading has lost several employees to OpenAI, while researchers from Two Sigma have joined Anthropic. Conversely, former Google DeepMind scientists have moved to Jump Trading and Citadel Securities.

The recruitment push reflects the growing importance of machine learning in systematic investing, with firms competing not only on compensation but also on access to computing infrastructure, research independence and opportunities to develop advanced predictive models.