Bayer is a global enterprise with core competencies in the Life Science fields of health care and agriculture. Its products and services are designed to benefit people and improve their quality of life. Bayer HealthCare combines the global activities of the Animal Health, Consumer Care, Medical Care and Pharmaceuticals divisions. Its aim is to discover, develop, manufacture and market products that will improve human and animal health worldwide. At Bayer you have the opportunity to be part of a culture where we value the passion of our employees to innovate and give them the power to change.

Master's Thesis (f/m/d) - Hybrid modeling for chromatography

Where do you want to go? What do you want to achieve? How would you like to get involved? The choice is yours and we will support you on your way. At Bayer, multi-talents and specialists, visionaries and passionate people, thinkers and doers come together to feed the world, slow climate change and create healthier, more sustainable lives for all. This is the opportunity to start your career with a global leader committed to #HealthForAll #HungerForNone. Bring your ideas, skills and passion with you. Your career starts here.

YOUR TASKS AND LEARNING AREAS

You develop hybrid models that combine partial differential equations with neural networks
You implement and test numerical algorithms for partial differential equations and optimization
You build a valuable network of contacts in the research and development community at Bayer
You prepare publications, presentations, and documentation of your work

WHO YOU ARE

You are enrolled as a master’s student in mathematics, physics, computational engineering science, or a comparable field
You have knowledge of numerical methods for partial differential equations and optimization algorithms, ideally with experience in their implementation
You possess good knowledge of at least one modern programming language such as Julia (preferred), C/C++, or Python
Ideally, you are familiar with Deep Learning or have hands-on experience with a Deep Learning framework, such as Lux/Flux, JAX, PyTorch, or TensorFlow
Experience in using Git and Linux is optional, but preferred
You demonstrate flexibility and the ability to work in a structured and independent manner
You enjoy analyzing complex interrelationships and tackling challenging questions
You are fluent in written and spoken English

WHAT WE OFFER

Our benefits package is flexible, appreciative, and tailored to your lifestyle, because: What matters to you, matters to us!

For a full-time position, you can expect an attractive salary of € 2.214 gross per month
Depending on the nature of your job, flexible work arrangements can be made in alignment with your manager
We support your growth through access to professional development and learning opportunities, such as LinkedIn Learning and our language learning platform Education First
As one of our perks, our Corporate Benefits program grants you access to sales discounts from more than 150 brands
We embrace diversity by providing an inclusive work environment in which you are welcomed, supported, and encouraged to bring your whole self to work

We look forward to your complete online application (cover letter, resume, certificate of study, and other relevant certificates) for a start date of approximately Mid of March 2026 for a period of at least 6 months or more.

YOUR APPLICATION

This is your chance to join us in addressing the greatest global challenges of our time: keeping people healthy, feeding the world's growing population and slowing climate change. You have a voice, ideas and perspectives that we want to hear. Because our success starts with you. Be part of it. Be Bayer.
Bayer welcomes applications from all people regardless of ethnicity, national origin, gender, age, physical characteristics, social origin, disability, union membership, religion, marital status, pregnancy, sexual orientation, gender identity or any other extraneous criterion under applicable law. We are committed to the principle of treating all applicants fairly and avoiding discrimination

Please note that your thesis must be supervised by a university on your part.

Location: Germany : North Rhine Westfalia : Leverkusen

Division: Crop Science

Reference Code: 860935