Vacancy No. 108/2024

Employee (f/m/d) in science, specializing in engineering or computer science

Job description

As part of your work, you will focus on the following tasks:

  • You will develop methods for image analysis (classification, segmentation and object tracking) in biology and biomedicine (focus: robust and adaptable deep learning methods incl. model monitoring) as well as for data fusion of image data, time series and metadata
  • Implementation of the methods in Python and integration into workflows of robot-assisted high-throughput processes
  • Application of the developed methods in close cooperation with working groups in the EU project TOXBOX
  • Supporting project coordination in the EU project TOXBOX
  • Collaboration in the supervision of Bachelor's and Master's theses
  • Publication of scientific results in scientific journals and at conferences and meetings

In addition to the scientific work, there is the possibility of a doctorate.

Personal qualification

You have a university degree (Master/ Diploma (University)) in the field of engineering, computer science, bioinformatics, or physics with initial experience in deep learning and in the application to biomedical data. Very good knowledge of the programming language Python and deep learning frameworks such as PyTorch round off your profile.

Entgelt

Salary category 13, depending on the fulfillment of professional and personal requirements.

Organizational unit

Institute for Automation and Applied Informatics (IAI)

Starting date

15.05.2024

Contract duration

limited to 3 years

Application up to

05.05.2024

Contact person in line-management

For further information, please contact Prof. Dr. Ralf Mikut, ralf.mikut@kit.edu

Application

Please apply online using the button below for this vacancy number 108/2024 .
Personnel Support is provided by 

Ms Wenz
phone: +49 721 608-25093,

Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

We prefer to balance the number of employees (f/m/d). Therefore we kindly ask female applicants to apply for this job.
Recognized severely disabled persons will be preferred if they are equally qualified.