Data Analyst (m/f/d) | Machine Learning for Medical Diagnostics

Einsatzort: Erlangen

MPI

  • Solid State Research & Material Sciences
  • Particle, Plasma and Quantum Physics
  • Immunobiology and Infection Biology & Medicine
  • Microbiology & Ecology
  • Structural and Cell Biology

Art der Stelle

Data Analyst (m/f/d) | Machine Learning for Medical Diagnostics

Stellenangebot vom 31.05.2022

The Max Planck Institute for the Science of Light (MPL) research covers a wide range of topics, including nonlinear optics, quantum optics, nanophotonics, photonic crystal fibers, optomechanics, quantum technologies, biophysics, and links between physics and medicine.

The Biological Optomechanics Division at the Max Planck Institute for the Science of Light is looking for a Data Analyst (m/f/d) - Machine Learning for Medical Diagnostics

About us

Cells are the basic entities of biological systems. They have particular physical properties, which enable them to navigate their 3D physical environment and fulfill their biological functions. We investigate these physical – mechanical and optical – properties of living cells and tissues using novel photonics and biophysical tools to test their biological importance. Our ultimate goal is the transfer of our findings to medical applications and to improve cell-based diagnostics. We are a highly interdisciplinary research group and value an open culture of knowledge transfer.

Project description

A large fraction of our research centers on real-time deformability cytometry (RT-DC), a high-throughput microfluidics-based imaging technique for cells. With RT-DC, we can rapidly measure vast numbers of cells, identify cell types (e.g. in blood), and characterize them optically (via their bright field image structure) and mechanically (via their deformation due to microfluidic shear stresses). We showed that RT-DC is sensitive to pathological changes during diseases, such as COVID-19, and we aim to expand our analysis tool set.

In this project, we are combining RT-DC data of patient blood with other, orthogonal imaging and quantification techniques, in an interdisciplinary team of lab technicians, physicians, and scientists. Your part in this project will be to forge machine-learning approaches and to devise visualization tools for diagnostic purposes.

Your profile

You are a data analyst with a background in image analysis and machine learning who is interested in (literally) bleeding-edge medical diagnostics techniques. You hold an advanced degree in computer science, mathematics, or physics (completed Master’s degree or higher at time of application). You are eager to engage with problems whose solutions are not yet mapped out. You want to broaden your background in medicine and explore uncharted medical territory.

You are fluent in Python and you have experience with numpy, scipy, and scikit-image. You know scikit-learn, tensorflow, or pytorch.. Ideally, you are already a Python package maintainer and you are familiar with test-driven development, the Python enhancement proposals (e.g. PEP8), and code documentation (e.g. with Sphinx). In your projects, you routinely use a distributed versioning control environment such as Git.

Your tasks

  • Image analysis and feature extraction using various data sources
  • Application of supervised and unsupervised machine learning techniques (such as SVMs, GMMs, Random Forests, Deep Neural Networks, ...) on imagery and tabular data for the identification of disease conditions
  • Development of data analysis pipelines with proper code documentation
  • Development of suitable visualization methods for the communication of findings (e.g., using dimensional reduction methods)

Application

Have we aroused your interest? Please submit your application via the application portal by the end of June 2022.

The Max Planck Society strives for gender and diversity equality. We welcome applications from all backgrounds. Furthermore, the Max Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals.