Research Associate

Ihre Aufgaben

• Contribution to the development of statistical methods
• Implementation of new methods in statistical software (preferably in R)
• Publication of results and their presentation at scientific conferences
• Support in writing reports to the funding organisation

Ihre Qualifikationen

• Master's degree (or equivalent) in Statistics, Mathematics, Biometry, Bioinformatics, or a related field
• Strong interest in the development and application of statistical and AI methods in medical research
• Solid background in statistical modeling and data analysis
• Basic knowledge in causal inference, explainable AI, or uncertainty quantification
• Good programming skills, preferably in R; experience with Python or other languages is an asset
• High motivation, creativity, and ability to work both independently and in a multidisciplinary team
• Good written and spoken English; knowledge of German is an advantage but not required

Wir bieten

• A challenging job in a multidisciplinary, co-operative team within the CAIMed consortium
• Diverse possibilities of internal and external professional training

Einleitungstext

Within the Faculty of Medicine and at the 'Göttingen campus', the Department of Medical Statistics (Chair: Professor Tim Friede) offers a statistical advisory service and teaching in the fields of biostatistics as well as bioinformatic analysis of clinical and genomic data. Members of the Department also conduct research in the fields of adaptive designs of clinical trials, meta-analysis/evidence synthesis, survival analysis, clinical epidemiology, and computational statistics.

Our department participates in CAIMed - the Lower Saxony research center for artificial intelligence (AI) and causal methods in medicine, which is funded by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the VolkswagenStiftung. CAIMed develops innovative methods for improved, personalized healthcare and contributes to the management of widespread diseases such as cancer, cardiovascular diseases, and infections. We are looking for a research associate to support the group "Statistical Evidence in AI Systems" working on the development of evidence synthesis procedures based on causal inference, counterfactual methods, explainability, uncertainty quantification, and/or federated learning.