Helmholtz Center for Information Security (CISPA)
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PhD or Postdoc Position in Mathematical Foundations of Machine Learning, Differential Privacy, Optimization, and Machine Unlearning CISPA Helmholtz Center for Information Security in Germany
Degree Level
PhD, Postdoc
Field of study
Computer Science
Funding
One fully funded position is available. CISPA offers strong funding for research and conference travel.
Country
Germany
University
Helmholtz Center for Information Security (CISPA)

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About this position
Andrew Lowy, Tenure-Track Faculty at CISPA Helmholtz Center for Information Security, is recruiting a PhD student or postdoctoral researcher to join his group in Saarbrücken, Germany.
The position is centered on the mathematical foundations of machine learning, with interests in differential privacy, optimization, and machine unlearning. The post also highlights broader directions in AI safety and alignment, especially for candidates interested in rigorous theoretical work, proving fundamental limits, and designing algorithms with guarantees.
Eligibility highlights: PhD applicants should have strong foundations in probability and linear algebra, plus excellent performance in proof-based courses such as real analysis. Prior theoretical research experience is especially welcome. Postdoc applicants should have a strong record of theoretical research in machine learning, optimization, or theoretical computer science.
Funding: one fully funded position is available, and CISPA provides strong support for research and conference travel.
How to apply: email [email protected] with a CV, a short research statement, transcripts (for PhD applicants) and/or 2–3 representative papers (for postdoc applicants), and contact information for 2–3 references.
Research environment: collaborative, international, and theory-oriented, with opportunities to shape the research direction jointly with the supervisor.
Funding details
One fully funded position is available. CISPA offers strong funding for research and conference travel.
What's required
PhD applicants should have strong foundations in probability and linear algebra, and excellent performance in rigorous proof-based courses such as real analysis. Prior theoretical research experience is particularly welcome. Postdoc applicants should have a strong record of theoretical research in machine learning, optimization, or theoretical computer science.
How to apply
Email [email protected] with your CV, a short research statement, transcripts for PhD applicants and/or 2–3 representative papers for postdoc applicants, plus contact information for 2–3 references.
More information can be found here
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