1 Postdoc (m/f/d) in Causal Inference / Statistics
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.
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. N-of-1 trials are a central part in the research projects of the department.
We are seeking one highly motivated scientist with expertise in Causal Inference and Statistics to join our team.
Tasks may include
- Develop and apply methodology for individual-level inference on the effect of health interventions
- Develop best practices and guidelines for causal inference in N-of-1 trials
- Develop and apply methodology to investigate treatment effect heterogeneity
- Collaborate with deep learning 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
- Implement the developed causal inference methodology for 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 and publications in Causal Inference and Statistics
- Expertise in programming languages such as R or Python
- Experience with open-source software development
- Strong communication skills and ability to work in interdisciplinary teams
We offer
- Opportunity to develop your own research profile within an exciting research area
- 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.
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1 Postdoc (m/f/d) in Causal Inference / Statistics Arbeitgeber: Dife
Das Deutsche Institut für Ernährungsforschung Potsdam-Rehbrücke (DIfE) ist ein hervorragender Arbeitgeber, der eine dynamische und internationale Forschungsumgebung bietet. Mit einem klaren Fokus auf interdisziplinäre Zusammenarbeit und exzellente Arbeitsbedingungen, einschließlich familienfreundlicher Regelungen und umfangreicher Entwicklungsmöglichkeiten, ermöglicht das DIfE seinen Mitarbeitern, ihre eigenen Forschungsschwerpunkte zu entwickeln und an innovativen Projekten zur personalisierten Gesundheitsförderung zu arbeiten. Zudem profitieren die Mitarbeiter von einer attraktiven Vergütung nach TV-L sowie zusätzlichen Leistungen wie einem Jobticket und 30 Tagen Urlaub.