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BA
Bayer
7d ago

Master's Thesis - Hybrid modeling for chromatography(m/w/x)

Leverkusen
from 2,214 / month
Full-timeInternshipOn-site

Description

In this role, you will bridge the gap between physics and AI by developing hybrid models while building a professional network within the company's research and development community.

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Requirements

  • •Enrolled master’s student in mathematics, physics, computational engineering science, or comparable field
  • •Knowledge of numerical methods for partial differential equations and optimization algorithms
  • •Good knowledge of Julia, C/C++, or Python
  • •Familiarity with Deep Learning or frameworks like Lux/Flux, JAX, PyTorch, or TensorFlow
  • •Optional experience in Git and Linux
  • •Flexibility and structured, independent work style
  • •Enjoyment of analyzing complex interrelationships and challenging questions
  • •Fluent written and spoken English
  • •Thesis supervision by a university

Education

Currently in higher education

Tasks

  • •Develop hybrid models combining differential equations and neural networks
  • •Implement numerical algorithms for differential equations and optimization
  • •Test numerical algorithms for differential equations and optimization
  • •Build a professional network within the R&D community
  • •Prepare publications and presentations of your findings
  • •Document your research and development work

Tools & Technologies

JuliaC/C++PythonLux/FluxJAXPyTorchTensorFlowGitLinux

Languages

English – Business Fluent

Benefits

Flexible Working

  • •Flexible work arrangements

Learning & Development

  • •Professional development
  • •LinkedIn Learning access
  • •Language learning platform

Corporate Discounts

  • •Corporate Benefits program

Informal Culture

  • •Inclusive work environment
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Bayer and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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