Tissue Engineering and Regenerative Medicine (Ertürk_Erener) 1

Department / Institute
Institute for Tissue Engineering and Regenerative Medicine (iTERM) (LMU & Helmholtz)
Subject area
Deep learning, artificial-intelligence (AI), graph neural networks, pattern recognition, spatial-omics
Project title
Image segmentation and spatial omics analysis for organ mapping
Name of supervisor
Dr. Ali Ertürk
Number of open positions
Language requirements
Very good command of written and spoken English
Academic requirements
Masters or Diploma degree in Life or Computer Sciences or a related field

Who we are:

We develop and implement 3D imaging technologies to be able to generate the highest resolution views of whole mouse bodies and human organs. We bring together cutting-edge science from biology, chemistry, engineering, and computer applications. Our overarching aim is to obtain a holistic view on inter-connected biological systems in health and disease. We also develop deep learning-based algorithms to analyze large imaging and molecular data in a robust and unbiased way. Our research received high attention from high-profile scientific journals (Nature Methods Pan, Cai…Ertürk, 2016; Nature Neuroscience Cai, Pan…Ertürk 2018; Cell Pan, Schoppe…Ertürk, 2019; Cell Zhao…Ertürk, 2020; Nature Methods Todorov, Paetzold…Ertürk, 2020) and media including New York Times, Wall Street Journal and Süddeutsche Zeitung. You can find more about us at www.erturk-lab.com.

Project description

We are seeking a highly motivated and talented PhD student in the area of computational image analysis to develop algorithms to chart the cellular and molecular map of the human heart using large imaging and spatial proteomics data. This ambitious project aims to unravel the cell and molecular level information from the heart using the state-of-the-art technologies including tissue clearing, light-sheet microscopy, proteomics and transfer learning. The candidate will work closely with the scientists from the biology side in the group and develop deep learning algorithms to segment cell level information from the whole human heart and analyse the 3D-imaging imaging data. The candidate will collaborate with the proteomics scientists in the team for AI-based analysis of proteomics data for different cardiac cell types. Furthermore, the candidate will work within an international consortium to contribute towards developing a common coordinate framework (CCF) for the healthy human body.

Your profile:

  • Diploma or M.S. degree in Life or Computer Sciences (or a related field)
  • Excellent knowledge of the state-of-the-art deep learning methods and modeling approaches
  • Interest in 3D imaging technology development and solving biomedically relevant problems with computational method
  • Track record with publications in medical image analysis with deep learning methods is a plus
  • Good communication skills in English, self-motivation, problem-solving and team player mentality
  • Always willing to learn, and have the courage to ask questions when not clear
  • Open to express opinions and receive feedbacks / critics
  • Ability to work independently, as well as part of a team

Our offer:

  • Work in an innovative, well-equipped, global and scientifically stimulating research organization
  • Gain insight into cutting edge technologies from biology to artificial intelligence
  • Refine your personal development with further training opportunities

What are you looking for?