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About the Role

This position focuses on conducting research in algorithmic machine learning, specifically contributing to the development of methods for automated mathematical discovery by integrating existing approaches with new methods from stochastic optimization. The project aims to extend previous work with new algorithmic approaches, strengthening core competencies at the intersection of algorithmic optimization and machine learning.

Responsibilities

• Developing and completing a novel sampling method based on stochastic optimization
• Combining diffusion bridges and temperature annealing to sample from Boltzmann distributions at low temperatures
• Adapting the method to problems in mathematical discovery
• Implementing, evaluating, and scaling the developed algorithmic approaches
• Synthesizing the results and preparing them for a scientific publication

Requirements

• Outstanding University degree (Master’s/Diploma) in mathematics, computer science, visual computing, or comparable subjects
• Expertise in the field of optimization, stochastic processes and mathematical discovery demonstrated through research and publications at conferences (e.g., ICML, ICLR, etc)
• Programming experience in Python and deep learning libraries such as PyTorch, SciPy, JAX, and related optimization toolchains
• Interest in exploring new research questions and driving their solutions toward practical implementation
• Interest in interdisciplinary collaboration, including applications at the intersection of geometry, topology and continuous optimization
• Very good English language skills

Skills

• algorithmic machine learning
• stochastic optimization
• mathematical discovery
• optimization
• stochastic processes
• Python
• PyTorch
• SciPy
• JAX
• geometry
• topology
• continuous optimization

Location

• Germany

Work Type

• full-time
• part-time

Experience Level

• remuneration group 13 TV-L

Education Level

• Master’s/Diploma

Salary/Compensations

• 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

Benefits

• friendly work environment
• flexible work and meeting times
• excellent equipment
• challenging professional environment
• active onboarding process
• professional training opportunities
• support in professional development
• additional pension scheme (VBL)
• 30 days annual leave
• flexible working hours (flexitime)
• capital allowance of up to € 150 per month
• 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

• Within the research group AI in Society, Science, and Technology
• ZIB’s core competencies at the intersection of algorithmic optimization and machine learning
• The developed sampling methods are intended to serve as scalable tools for other interdisciplinary and data-intensive research projects at ZIB.

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.