Position: PhD Candidate in Interactive Machine Learning

Department: Junior Research Group Interactive Machine Learning

Code number: 2021-0103

The German Cancer Research Center is the largest biomedical research institution in Germany. With more than 3,000 employees, we operate an extensive scientific program in the field of cancer research.

Job description:

Considering human interaction when designing machine learning (ML) systems bears great potential: On the one hand, decision-making in ML systems remains imperfect in practice, thus requiring human interaction for mission-critical applications such as clinical diagnostics. On the other hand, the burden of manual training data annotation can be alleviated by means of human-in-the-loop scenarios.

Taking this human-centered perspective, the Interactive Machine Learning Group headed by Paul Jaeger strives to pioneer ML research directed at real-life applications. Specifically, our research involves probabilistic modeling, explainable AI, user modeling, active learning, and interactive systems with a special focus on image analysis tasks such as object detection or segmentation. A further interest lies in the appropriate and application-oriented evaluation of ML systems. 

The German Cancer Research Center (DKFZ) provides a highly attractive research environment and high-impact application opportunities for our ML methodologies. Moreover, our group is part of the Helmholtz Imaging Platform, an initiative towards leveraging image processing synergies across all Helmholtz research centers. As such, we will work with diverse domain-experts to tackle grand societal challenges and design human-centered ML systems for unique imaging tasks from all across Helmholtz. The group collaborates globally with top-notch ML experts and is part of a thriving AI community in and around Heidelberg by running heidelberg.ai and holding close connections to Heidelberg University as well as the ELLIS Unit Heidelberg. As part of the DKFZ-Data-Science initiative, our group has access to state-of-the-art ML computing infrastructure.

 

Requirements:

We are looking for a highly motivated outstanding student curious about fundamental research questions related to machine learning and how it can benefit society. Further requirements:

  • Master's degree in computer science, physics, mathematics, or related field
  • Strong math background and coding experience
  • Passionate about deep learning and high impact applications 
  • Collegial attitude and communication skills

To apply, please submit a cover letter indicating your personal motivation for this position, curriculum vitae, transcripts of previous studies, as well as names and e-mail addresses of two references.

We offer:

  • Interesting, versatile workplace
  • International, attractive working environment
  • Campus with modern state-of-the-art infrastructure
  • Access to international research networks
  • Doctoral student payment including social benefits
  • Flexible working hours
  • Comprehensive training and mentoring program through the Helmholtz International Graduate School

Earliest Possible Start Date: as soon as possible

Duration: The position is limited to 3 years with the possibility of prolongation.

Application Deadline: 28.04.2021

Contact:

Dr. Paul Jäger
Phone +49 (0)6221/42-3015

Please note that we do not accept applications submitted via email.


The DKFZ is committed to increase the proportion of women in all areas and positions in which women are underrepresented. Qualified female applicants are therefore particularly encouraged to apply.

Among candidates of equal aptitude and qualifications, a person with disabilities will be given preference. 

To apply for a position please use our online application portal (https://www.dkfz.de/en/stellenangebote/index.php).

We ask for your understanding that we cannot return application documents that are sent to us by post (Deutsches Krebsforschungszentrum, Personalabteilung, Im Neuenheimer Feld 280, 69120 Heidelberg) and that we do not accept applications submitted via email. We apologize for any inconvenience this may cause.