Science Manager for Research Funding and Grant Acquisition | Science Manager (m/w/d) Drittmittelakquise

TU München Zentrum Mathematik

Garching bei München, Bayern, Deutschland
Published Mar 19, 2026
Full-time
Fixed-term

Job Summary

The Munich Center for Machine Learning (MCML), a joint initiative of LMU and TUM, is seeking a Science Manager to lead third-party funding acquisition. In this role, you will work closely with leading researchers to develop strategies for securing national and international grants (e.g., EU, DFG). Your daily responsibilities include identifying funding opportunities, coordinating complex grant applications, managing budgets, and liaising with legal departments for contract negotiations. You will act as a bridge between scientific research and administrative execution, ensuring project management remains compliant with funding guidelines. This position is ideal for someone with a strong scientific background in AI/ML who enjoys networking and strategic planning. The role offers a collaborative environment within Germany's AI excellence network, flexible working hours with home-office options, and professional development opportunities within the public service pay scale (TV-L E13).

Required Skills

Education

Successfully completed university degree (Master's or equivalent) in Machine Learning, Data Science, or a related field; a PhD is ideally preferred.

Experience

  • Proven experience in independent competitive third-party funding acquisition (e.g., EU funding, DFG).
  • Professional experience in project management, ideally within research or industrial projects.
  • Demonstrated experience in networking and interdisciplinary collaboration.
  • Experience in creating scientific and administrative reports and presentations.
  • Intercultural competence, potentially gained through extended stays abroad.

Languages

German (Fluent)English (Fluent)

Additional

  • The position is initially limited to 2 years. Candidates must be willing to work at least 30 hours per week. Applications must be submitted as a single PDF file by April 15, 2026.