International PhD Position in Causal Machine Learning, Netherlands

  •  PhD
  •  05-Dec-2024
  •   Netherlands
  • $$  Fully-funded (salary and benefits in accordance with the Collective Labour Agreement for Dutch Universities, scale P)

Eindhoven University of Technology (TU/e) invites applications for International PhD Position in Causal Machine Learning. This four-year position focuses on cutting-edge research in causality, including areas such as causal representation learning, counterfactual reasoning, and applications in AI.

The program is open to motivated candidates with a strong academic background in Computer Science, Mathematics, or related fields. Applicants should demonstrate excellent analytical, coding, and communication skills and a desire to conduct impactful research.Eindhoven University of Technology (TU/e), located in the Netherlands, is a leading research-driven university specializing in engineering, technology, and innovation. Renowned for its collaborative approach with industry and academia, TU/e focuses on solving complex global challenges through interdisciplinary research and education. Its vibrant, green campus fosters innovation and entrepreneurship, making it a hub for cutting-edge developments in areas like artificial intelligence, sustainable energy, and smart mobility.

Technische Universiteit Eindhoven Information

Technische Universiteit Eindhoven Grants

International PhD Position in Causal Machine Learning, Netherlands Founded in 1956, Technische Universiteit Eindhoven (Eindhoven University of Technology) is a non-profit public higher education institution located in the urban setting of the large town of Eindhoven (population range of 50,000-249,999 inhabitants), North Brabant. Officially accredited and/or recognized by the Ministerie van Onderwijs, Cultuur en Wetenschap, Nederland (Ministry of Education, Culture and Science, Netherlands), Technische Universiteit Eindhoven (TUe) is a medium-sized (uniRank enrollment range: 7,000-7,999 students) coeducational higher education institution. Technische Universiteit Eindhoven (TUe) offers courses and programs leading to officially recognized higher education degrees in several areas of study. See the uniRank degree levels and areas of study matrix below for further details.

Eligibility Criteria

  • Eligible Countries: Open to all nationalities
  • Acceptable Course or Subjects: The scholarship will support research in Causal Machine Learning and related areas offered by TU/e.
  • Admissible Criteria: Applicants must:
    • Hold a Master’s degree in Computer Science, Mathematics, or a related field.
    • Possess strong analytical, coding (e.g., Python, PyTorch, Tensorflow), and academic writing skills.
    • Demonstrate proficiency in English (written and spoken).
    • Exhibit the ability to work independently and collaboratively, with a willingness to contribute to teaching tasks (10% of employment time).
    • Be motivated to publish research in high-quality conferences and journals.

Offered Benefits

  • Full-time employment for four years, subject to an intermediate evaluation after nine months.
  • Monthly salary ranging from €2,770 to €3,539.
  • Year-end bonus of 8.3% and annual vacation pay of 8%.
  • Access to TU/e’s high-quality training programs, technical infrastructure, on-campus childcare, and sports facilities.
  • Allowances for commuting, working from home, and internet costs.
  • Support from a Staff Immigration Team and eligibility for the 30% tax compensation scheme for international candidates.

Application Process

Applicants must submit a complete application through the university’s dedicated web system (applications via email will not be accepted). The application should include:

  1. A research statement (max. 1 page) outlining research experience, interests, and preferred future research direction.
  2. A CV detailing education history, relevant courses, theses, research experience, and teaching experience (if applicable).
  3. Transcripts of Bachelor’s and Master’s degrees.
  4. An electronic copy of the Master thesis.
  5. Electronic copies (or links) to publications or equivalent materials.
  6. Contact details or recommendation letters from two referees.
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