Postdoc (m/f/div) in Deep Learning / Reinforcement Learning

The newly established Department of Computational Precision Nutrition (CPN) invites applications for a

Postdoc (m/f/div) in Deep Learning / Reinforcement Learning

starting as soon as possible.

The Department of Computational Precision Nutrition develops methods and software for the analysis of both population-level and individual-level data to enable personalized health recommendations based on dietary patterns, behaviors, and diet-associated biomarkers. We aim to contribute to the personalized prevention and treatment of chronic diseases and advance our understanding of the mechanisms underlying their development. Two methodological focus areas are digital N-of-1 trials and deep learning-based modeling of multimodal biomedical data.

We are seeking one highly motivated scientist with expertise in deep learning / reinforcement learning to join our team.

Tasks include:

• Developing, implementing, and applying digital twins and RL agents based on multimodal health data for personalized recommendations of dietary and other health behavior to improve health
• Collaborating with causal inference researchers to jointly develop methods for analyzing multimodal digital N-of-1 trials (patient reported outcomes, wearables, images, audio, omics data) linking causal inference and deep learning
• Developing methodology for individual-level inference of large epidemiological studies (e.g., EPIC Potsdam study, German National Cohort study), including omics data
• Implementing the developed causal inference methodology for studies run on the StudyU platform with collaborators in Germany, Australia, USA, South Korea, and Ghana
• Developing clear data visualizations, reports, and written summaries to communicate results – for scientific publications but also for study participants and patients
• Collaborating with clinicians, epidemiologists, software developers, and laboratory scientists in the design of new studies and analysis of existing data

Skills and requirements:

• Excellent master’s and doctoral degree with demonstrated expertise in deep learning / multimodal learning / reinforcement learning
• Publication record at top machine learning conferences
• Expertise in programming languages, such as R or Python
• Experience with advanced deep learning frameworks and open-source software development
• Strong communication skills and ability to work in interdisciplinary teams

We offer:

• Opportunity to develop your own research profile among the exciting research topics described above
• A dynamic, international, and interdisciplinary research environment as well as excellent working conditions and outstanding technical equipment
• Employment with remuneration according to TV-L, level 13, plus annual special payment and company pension scheme
• Family-friendly working conditions (certificate “audit berufundfamilie”)
• Supporting of mobility with a jobticket for using the public transport
• Location close to the vibrant city of Berlin, with easy accessibility by public transport or car
• 30 days of vacation
• Participation in the benefits program for employees (“corporate benefits”)

The advertised position is available for initially three years.

We promote the employment of people with severe disabilities and are committed to equal opportunities for them. Applicants with severe disabilities will be given preferential consideration if they have the same qualifications.

We look forward to your application!

Please send your documents (cover letter, CV, copies of degrees / certificates, and references) with reference number 2026_W09_AP as a single PDF file before October 15th, 2026, via e-mail to jobs@dife.de.

For further information please contact
Prof. Dr. Stefan Konigorski
Head of the Department of Computational Precision Nutrition
E-mail: stefan.konigorski@dife.de

We process your application documents for the purpose of carrying out the application procedure in accordance with Art. 6 para. 1 lit. b) GDPR, Art. 88 GDPR. For more information on the collection, processing, and use of personal data by the German Institute of Human Nutrition as part of the application process and your rights under data protection law, please contact the Department of Human Resources and Social Services (jobs@dife.de).