Browse
···
Log in / Register
Summary: This PhD position focuses on building and validating a thinking-with-tools approach for LLMs to enhance data-driven decisions and human-AI collaboration. Highlights: 1. Opportunity to shape the future of AI decision-making 2. Work at the intersection of innovation and impact 3. Develop high-quality software and create sustainable value #### **Mission** **I****ndustry hire at Aily Labs, academic PhD supervision at EURECOM (Prof. Paolo Papotti)** Enterprises are turning to LLMs for high\-stakes, data\-driven decisions over structured data, such as databases and knowledge graphs. This PhD sets a three\-year agenda to build and validate thinking\-with\-tools, an approach where the model embeds live tool use (e.g. SQL/SPARQL, Python) inside its reasoning. Beyond accuracy gains, the goal is an efficient decision system that knows when it’s unsure, asks for clarification, and escalates when judgment matters. Consider the question: “If we cut Product X’s price by 5% next quarter, what revenue should we expect?”, the system reasons, pulls the right numbers, exposes its assumptions, and invites a human to confirm or adjust when needed. This aligns with broader research priorities on human\-AI decision support and governance in complex settings. We will keep an open, comparative stance: tool\-augmented reasoning is a promising solution, not a foregone conclusion. A core part of the PhD is to demonstrate, on company data and realistic scenarios, when it surpasses agentic pipelines (LLMs orchestrating tools step\-by\-step) in terms of quality of the solutions, efficiency, costs, and their trade\-offs. You will design the framework, fine\-tune open LLMs to plan and run safe, efficient tools, and develop policies that balance accuracy, cost, and latency. This includes building a sandbox reflecting enterprise schemas; craft training traces that interleave reasoning and tool calls; and prototype realistic Aily Labs use cases (e.g., forecasting and customer insights). Examples of possible research outcomes are: a cost\-aware reasoning optimizer that learns when/how to invoke tools; typed, auditable traces and human\-in\-the\-loop mechanisms (uncertainty, clarification, escalation); and budget\-aware routing that adapts across schemas and tasks. Impact will be measured with clear evaluations of accuracy/latency/cost trade\-offs, robustness to schema evolution, and governance considerations. **Environment.** In this industrial PhD you’ll be hired by Aily Labs and embedded with product/data teams in Barcelona/Madrid (Spain), while enrolled at EURECOM under Prof. Paolo Papotti supervision. Duration 36 months; working language English. #### **Your profile** Strong background in ML/NLP and databases (SQL/DBMS/KGs); solid Python and experimentation. Experience with LLM fine\-tuning, text\-to\-SQL, tool use/agents, or RL is a plus. **How to apply.** Send a CV, transcripts, a short research statement (1 page), and representative publications/software (e.g., GitHub) to papotti@eurecom.fr (cc alaa.boukhary@ailylabs.com and gerard.conangla@ailylabs.com). Applications are reviewed on a rolling basis. **\*\*Valid authorization to work in Spain (no visa sponsorship available)**#### **Who are we?** If you’re ready to shape the future of AI decision\-making and work at the intersection of innovation and impact, join us! * We are an internationally diverse and dynamic team with more than 30 nationalities. * We are hybrid, 2 days a week, in office. * We develop high\-quality, beautiful software, and thus create sustainable added value for our customers. * We live an open feedback culture so that we can constantly reflect and improve. #### **About us** LEARN MORE
