1 Postdoc (m/f/d) in Deep Learning / Reinforcement Learning

Arbeitsagentur; Bergholz-Rehbrücke; Vor Ort; EUR 54.000 - 66.000; Vollzeit

Zusammenfassung

The German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) invites applications for 1 Postdoc (m/f/d) in Deep Learning / Reinforcement Learning. The department focuses on digital twins and multimodal data to provide personalized dietary health recommendations.

The role involves collaboration with clinicians, epidemiologists, and software developers on world-class nutrition research. Starting as soon as possible, the position is initially 3 years, with TV-L level 13 remuneration and

Qualifikationen

• Excellent master and doctoral degree with demonstrated expertise in Deep Learning / Multimodal Learning / Reinforcement Learning.
• Publication record at top machine learning conferences.
• Experience with programming languages such as R or Python.
• Experience with advanced deep learning frameworks & open-source software development.
• Strong communication skills and ability to work in interdisciplinary teams.

Aufgaben

• Develop, implement and apply digital twins and RL agents based on multimodal health data for personalized recommendations of dietary and other health behavior to improve health.
• Collaborate with causal inference researchers to jointly develop methods for analyzing multimodal digital N-of-1 trials (patient reported outcomes, wearables, images, audio, omics data).
• Develop methodology for individual-level inference of large epidemiological studies including omics data.
• Implement models on StudyU platform with collaborators in Germany, Australia, USA, South Korea and Ghana.
• Develop clear data visualizations, reports, and written summaries to communicate results for publications and study participants.

Kenntnisse

Deep Learning

Reinforcement Learning

Multimodal Learning

Publications

Ausbildung

Master's degree

Doctoral degree

Tools

PyTorch

TensorFlow

R

Python

Jobbeschreibung

The German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) is a member of the Leibniz Association. The institute’s mission is to conduct experimental and clinical research in the field of nutrition and health, with the aim of understanding the molecular basis of nutrition-dependent diseases, and of developing new strategies for treatment and prevention.

The newly established Department of Computational Precision Nutrition (CPN) invites applications for 1 Postdoc (m/f/d) 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.

Tasks include

• Develop, implement and apply digital twins and RL agents based on multimodal health data for personalized recommendations of dietary and other health behavior to improve health
• Collaborate 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
• Develop methodology for individual-level inference of large epidemiological studies (e.g., EPIC Potsdam study, German National Cohort study) including omics data
• Implement the developed models for multimodal studies run on the StudyU platform with collaborators in Germany, Australia, USA, South Korea and Ghana
• Develop clear data visualizations, reports, and written summaries to communicate results – for scientific publications but also for study participants and patients
• Collaborate with clinicians, epidemiologists, software developers and laboratory scientists in the design of new studies and analysis of existing data

Skills and requirements

• Excellent master 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 & 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 3 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.

For further information please contact

Prof. Dr. Stefan Konigorski
Head of the Department of Computational Precision Nutrition
E-Mail: [e-mail]

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 ([e-mail]).