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Researcher (f/m/d)

Arbeitsbeschreibung - Researcher (f/m/d)

Position Details

Within the third-party funded project “Agentic Dual Verification and Dual Reduction Discovery for Mathematical Optimization” in the research group Interactive Optimization and Learning (IOL), Department AI in Society, Science, and Technology, we are offering a position, starting as soon as possible, for a Researcher (f/m/d) on a full-time basis (39,4 hours per week), limited until September 30, 2028. If the applicant meets the relevant wage requirements and personal qualifications, the salary will be based on remuneration group 13 TV-L of the pay scale for the German public sector.

Background & Context

Large Language Models (LLMs) and agentic AI systems are creating new opportunities to enhance optimization-based decision-making. This project investigates how AI agents can support mathematical discovery in optimization by automatically generating and verifying dual reductions and valid inequalities, two key components for improving optimization solver performance. Combining mathematical optimization, automated reasoning, and AI-driven search, the project aims to develop methods that can propose, test, and certify new mathematical structures with rigorous correctness guarantees. Embedded in the MATH+ research environment, the project contributes to advancing both the foundations of mathematical optimization and the development of next-generation intelligent solver technologies.

Your Tasks

The successful candidate will conduct research at the intersection of mathematical optimization, artificial intelligence, and automated reasoning. The work combines theoretical foundations, algorithm design, and software development to create AI-assisted methods for the generation and verification of dual reductions and valid inequalities in optimization. The position includes both methodological research and practical implementation, encompassing the development, evaluation, and application of novel approaches on large-scale computational systems and challenging optimization problems.
  • Conduct research on model alignment, data generation, and data distillation techniques to enhance the ability of LLMs to effectively use mathematical and optimization software, including MILP solvers, SAT solvers, constraint programming solvers, and optimization heuristics.
  • Investigate LLM-based agentic systems for optimization solvers and develop reasoning and planning frameworks capable of addressing a broad range of optimization and operations-research problems.
  • Design, develop, and implement software tools and infrastructure, including novel solver interaction tools for LLM agents.
  • Publish research findings in leading conference and journal venues.

Your Profile

  • Outstanding university degree (Master's or Diploma) in Computer Science, Mathematics, Data Science, Operations Research, Machine Learning, or a related field.
  • Strong background in at least one of the following areas:

    • Mathematical optimization, operations research, heuristics, approximation algorithms, and related optimization methods; or
    • Machine learning and artificial intelligence, including deep learning, reinforcement learning, representation learning, and agentic AI systems.
  • Experience in developing and implementing advanced algorithms, machine learning models, or optimization methods.
  • Excellent programming skills in Python and experience with modern software development practices (including debugging, testing, and Linux environments). Experience with Julia and/or machine learning frameworks such as PyTorch, TensorFlow, or Keras is a plus.
  • Strong interest in pursuing novel research questions and translating theoretical ideas into practical implementations.
  • Experience with AI tools and computational methods in research and development workflows.
  • Interest in interdisciplinary collaboration at the interface of mathematics, optimization, and artificial intelligence.
  • Excellent communication and teamwork skills, a high degree of independence, initiative, and commitment, and the willingness to publish research results at an international level.

What you can expect

We offer a friendly work environment with flexible work and meeting times, excellent equipment and a challenging professional environment
 
as well as
 
  • comprehensive training in a competent and cooperative team,
  • professional training opportunities and support in professional development,
  • an additional pension scheme (VBL),
  • 30 days annual leave, flexible working hours (flexitime),
  • a salary based on TV-L (collective agreement for the public service of the federal states) in accordance with qualifications and professional experience with annual bonus payment,
  • capital allowance of up to € 150 per month, or alternatively a BVG job ticket plus the remaining balance,
  • the use of canteens and sports programs of the Freie Universität Berlin (FUB) at reduced rates. 
 
The Zuse Institute Berlin is committed to diversity, equal opportunities, and a respectful and inclusive working environment. In order to increase the proportion of women in areas where they are underrepresented, we expressly encourage women to apply. Irrespective of this, we welcome applications from all qualified candidates regardless of age, disability, ethnic or social background, religion or belief, sexual orientation, or gender identity.
 
Please apply via our online application form by August 19, 2026, submitting your complete application including curriculum vitae in tabular form and the standard supporting documents.
Our private policy statement regarding application data is available at www.zib.de/impressum.
 
For further job offers please visit our website at www.zib.de/jobadvertisement.

Contact

For further information about the position, please refer to our website www.zib.de or contact Mr. Max Zimmer ([email protected]).

About us

The Zuse Institute Berlin (ZIB) is an interdisciplinary research institute for applied mathematics and data-intensive high-performance computing. Its research focuses on modeling, simulation and optimization with scientific cooperation partners from academia and industry.

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