Master thesis

"Advancing Quantitative Microscopy with Machine Learning"

About the Project: In the realm of biomedical microscopy, machine learning (ML) methods have emerged as game-changers, often surpassing conventional image processing techniques. Our mission is to explore the vast potential of ML approaches in the realm of quantitative microscopy. Specifically, we aim to enhance and refine ML-based denoising methods to revolutionize quantitative analysis strategies.

Join Our Dynamic Team: Become an essential part of the Cellular Neurophysiology team at Hannover Medical School, equipped with cutting-edge laboratory facilities. Contribute to a captivating and highly pertinent research project, guided by our seasoned experts. We offer unwavering support to ensure your project's triumphant completion. Our expertise spans various research methodologies, including:

  • State-of-the-Art Microscopy Techniques
  • Advanced Analytical Image Processing and Analysis Strategies
  • ML-Based Image Processing
  • Access to the MHH High-Performance Computing (HPC) Cluster

Your Role: As a Master's thesis candidate, you will build upon a recently successful Master's thesis focused on ML-based denoising approaches for microscope data characterized by Poissonian noise. Your task is to expand and adapt these methods to low-light conditions, incorporating additional Gaussian noise components.

Your Profile: We are searching for candidates who meet the following qualifications:

  • Hold a Bachelor's degree in a relevant field (e.g., computer science, machine learning, microscopy)
  • Exhibit a strong motivation and dedication to the project
  • Demonstrate a profound interest in applying ML-based techniques to enhance quantitative microscopy
  • Possess a keen aptitude for machine learning
  • Proficient in programming languages such as Python or Matlab
  • Expertise in ML application frameworks such as TensorFlow or PyTorch
  • Flourish in a collaborative, team-oriented, and international research environment

Application Requirements: To apply, please provide the following documents:

  • A cover letter detailing your background and motivation
  • A concise CV (1 page)
  • A copy of your Bachelor's degree certificate
  • A copy of your high school certificate

Application Process: Feel free to submit your application anytime via email to:

Join us in advancing the field of quantitative microscopy through the power of machine learning. Apply today and be part of an exciting journey in international research!