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About the Role

This position is associated with the Math+ funded project “Solution Techniques for Mixed-Integer Nonconvex Optimization,” focusing on developing new algorithmic techniques for solving mixed-integer nonlinear optimization problems with non-convex objectives within a Branch-and-Bound framework. The goal is to enable globally optimal and scalable solutions by combining methods such as convexification, spatial branching, and first-order optimization approaches.

Responsibilities

• Develop new algorithmic techniques for solving mixed-integer nonlinear optimization problems with non-convex objectives within a Branch-and-Bound framework.
• Enable globally optimal and scalable solutions by combining methods such as convexification, spatial branching, and first-order optimization approaches.
• Conduct experimental evaluation and benchmarking.

Requirements

• Completed university degree (Master’s or equivalent) in Mathematics, Computer Science, Operations Research, or a related field.
• Strong background in optimization (especially MINLP), convex/non-convex analysis, and algorithm design.
• Experience with scientific programming (preferably Julia; alternatively Python, C++, etc.).
• Familiarity with optimization frameworks and solvers (e.g., MIP, SCIP).
• Knowledge of Branch-and-Bound, first-order methods (e.g., Frank-Wolfe), and global optimization techniques.
• Experience with experimental evaluation and benchmarking.
• Interest in interdisciplinary collaboration.
• Very good English language skills.

Skills

• Optimization (MINLP)
• Convex/non-convex analysis
• Algorithm design
• Scientific programming (Julia, Python, C++)
• Optimization frameworks and solvers (MIP, SCIP)
• Branch-and-Bound
• First-order methods (Frank-Wolfe)
• Global optimization techniques
• Experimental evaluation
• Benchmarking

Location

• Germany

Work Type

• Full-time
• Limited contract

Experience Level

• Master’s degree or equivalent

Education Level

• Master’s degree or equivalent in Mathematics, Computer Science, Operations Research, or a related field

Salary/Compensations

• Remuneration group 13 TV-L of the pay scale for the German public sector

Benefits

• Friendly work environment
• Flexible work and meeting times
• Excellent equipment
• Challenging professional environment
• Active onboarding process
• Varied, future-oriented and responsible field of activity
• Professional training opportunities and support in professional development
• Additional pension scheme (VBL)
• 30 days annual leave
• Flexible working hours (flexitime)
• Annual bonus payment
• Capital allowance of up to €150 per month, or alternatively a BVG job ticket plus the remaining balance
• Use of canteens and sports programs of the Freie Universität Berlin (FUB) at reduced rates

About the Company

• Part of the research group Interactive Optimization and Learning (IOL) within the department AI in Society, Science, and Technology.
• Associated with the Math+ funded project “Solution Techniques for Mixed-Integer Nonconvex Optimization.”
• Close ties to universities and research institutes in the region due to involvement in major regional cooperative projects such as the Einstein Center for Mathematics (ECMath), the DFG Cluster of Excellence MATH+, the Berlin Mathematical School (BMS) or the Berlin Big Data Center (BBDC).

Equal Opportunity

• Applicants with disabilities will be given preference if equally qualified.
• Female applicants are highly encouraged to apply, since women are under-represented in natural sciences and ZIB seeks to increase the proportion of women in this field.