IBM Research Europe – Zurich Lab 
Rüschlikon, Switzerland Resumen de la oferta
IBM Research Europe – Zurich Lab in Switzerland is offering a Master's Thesis position titled 'Neural Solvers for Mixed-Integer Nonlinear Programming.' This role focuses on developing neural optimization approaches to solve computationally challenging mixed-integer nonlinear programs (MINLPs) faster than conventional algorithms. The candidate will investigate end-to-end surrogate model workflows combining deep neural networks with feasibility restoring layers based on the GENCO model. Applications will be demonstrated on electric power systems, specifically topology optimization under AC optimal power flow. Ideal candidates possess a Bachelor’s degree in computer science, engineering, or mathematics, with strong skills in Python, PyTorch, and optimization frameworks like Gurobi or Pyomo. The position offers access to state-of-the-art computing resources and a collaborative, multicultural research environment dedicated to innovation and scientific publication.
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Descripción del proyecto
Master's Thesis
Neural Solvers for Mixed-Integer Nonlinear Programming
Ref. 2026_025
Mixed-integer nonlinear programs (MINLPs) are computationally challenging and remain difficult to solve at speed and scale. In this master thesis, the candidate will investigate neural optimization approaches that learn to generate near-optimal solutions to MINLPs significantly faster than conventional optimization algorithms, while respecting hard physical and engineering constraints.
In particular, the thesis will investigate end-to-end surrogate model workflows that combine deep neural networks to solve the combinatorial (MILP) and continuous (NLP) problem in sequence. The NLP part will build on our GENCO model, by the extension of feasibility restoring layers. The proposed concepts will be demonstrated and benchmarked on electric power systems, for topology optimization under AC optimal power flow.
The ideal candidate will bring a strong foundation in machine learning, optimization, or applied mathematics, and the motivation to advance the algorithmic frontier on industrially relevant large-scale optimization problems.
Why Join Us?
You will join a world-class research environment and collaborate closely with researchers in AI, optimization, and energy systems. You will gain access to state-of-the-art machine learning frameworks, optimization software, and high-performance computing resources. Our environment encourages innovation and supports scientific publications and intellectual property creation.
Preferred Qualifications
Bachelor’s degree in computer science, electrical engineering, mathematics, operations research, or a related field.
Background in one or more of the following domains: machine learning/AI, optimization, mathematical programming, power systems, control, or applied mathematics.
Strong programming skills in Python and PyTorch; experience with scientific computing and deep learning on compute clusters.
Experience with optimization frameworks (e.g., Gurobi, Pyomo, JuMP, IPOPT) is a plus.
Self-motivated, team-oriented, and eager to work on interdisciplinary research problems.
Diversity & Work Environment
IBM is committed to fostering diversity and inclusion in the workplace. You will join an open, multicultural research environment that values different perspectives and supports flexible working arrangements. Our goal is to help all genders and backgrounds thrive professionally while maintaining a healthy work–life balance.
How to Apply
If you are interested in this position, please submit your application through the link below.
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