Master Thesis: Mathematical optimization using evolutionary algorithms

Master thesis: Mathematical optimization using evolutionary algorithms

A Snapshot of Your Day

In Siemens Energy, we are developing many solutions that rely on mathematical optimization as their kernel. Such solutions aim to optimize different processes such as customers operations or our production plans. We are currently dependent on external companies for our solvers for mathematical optimization. Therefore, comes the need to investigate other ways for solving optimization problems, such methods like Genetic Algorithms based optimization methods could be the solution.

How You’ll Make an Impact

Your role in this project is to try different optimization methods that focus on Genetic Algorithms (probably using libraries like DEAP python Library) and investigate the business impact of your new proposed solution and how it is helpful for the company compared to the currently developed optimization method. Your tasks will be:
* Literature Review of existing methodologies used for formulating and solving optimization problems.
* Build and test different solutions and evaluate their performance.
* Understand and present the business impact of your solution in comparison to the company’s current optimization methods.
* Validate the methodology with current optimization method.

What You Bring 
* Academic background in energy systems, engineering, computer science, statistics, mathematics, or another relevant field
* Strong analytical skills and solid mathematical background and must have programming skills (preferable: Python, R)
* Introductory knowledge of Mathematical optimization, like linear programming and heuristics methods is a must-have
* We consider meritorious knowledge of more complex optimization techniques, Genetic Algorithms
* We consider meritorious skills the knowledge of machine learning/deep learning-oriented libraries (scikit-learn, caret, mlr, keras, pytorch etc…), data handling libraries (Pandas or tidyverse)

About the Team

You will get an opportunity to work with ED&AA (Enterprise Data and Advance Analytics) team, which is one of the teams in IT that is driving the internal digital transformation of Siemens Energy to generate business value. Team consists of approximately 25 data scientists and data analysts from all around the world. You will be mentored by experienced specialist within the area. You will get to know a global company in the forefront of new energy technology. During your time at Siemens Energy you will also get the opportunity to establish network for future cooperation.

Who is Siemens Energy?

At Siemens Energy, we are more than just an energy technology company. We meet the growing energy demand across 90+ countries while ensuring our climate is protected. With more than 92,000 dedicated employees, we not only generate electricity for over 16% of the global community, but we’re also using our technology to help protect people and the environment.

Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.

Our Commitment to Diversity

Lucky for us, we are not all the same. Through diversity, we generate power. We run on inclusion and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability. We energize society, all of society, and we do not discriminate based on our differences.

Application

Don’t hesitate – apply via https://www.siemens-energy.com/global/en/company/jobs.html, id nr 237339 not later than November 13^th.

Ongoing selection is applied, the role might be filled before last application date.

For questions about the role, please contact the recruiting manager Davood Naderi on davood.naderi@siemens-energy.com.

For questions about the recruitment process please c...