Institute of Computational Biology
Area of research:
As German Research Center for Environmental Health, Helmholtz Zentrum München pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes mellitus, allergies and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle.
The Institute of Computational Biology is a globally recognized department for innovations in data analysis and modeling of biological systems, anchored at the Helmholtz Zentrum Munich and the Technical University Munich.
The "Machine Learning for Optimization of Patient Treatments" group at the Helmholtz Center Munich (HMGU) is seeking to hire multiple talented and motivated individuals at the Postdoc- or PhD Student-level to join our team to develop new data analytics for the optimization of patient pathways, using big data analysis methods on electronic medical records (EMR).
We are looking for aPostdoctoral Fellowship or PhD Student Position (m/f/diverse) in Machine Learning for Patient Data Analysis
This project is a joint collaboration between the Johns Hopkins University (JHU; Baltimore MD, USA) and the HMGU (Munich, Germany). The primary location for our team is at the HMGU Munich center. Our team members will have opportunity to visit and work with faculties and researchers at the Johns Hopkins University and Hospital.
Our HMGU-funded project currently has access to 10 years of comprehensive EMR data of patients who underwent cardiac surgery at the Johns Hopkins Hospital (Baltimore MD, USA). Furthermore, we work with other national-level databases within the scope of this project.
Our goal is to model, predict, and find causal factors related to the outcomes of interest, such as length of stay, prolonged intubation, blood transfusion, and renal failure. In addition to project-specific work, you will have the opportunity to mentor students at the graduate and undergraduate level, and may initiate and pursue research on supplemental ideas in collaboration with faculty aligned with their interests.
Your QualificationsMSc or PhD degree in computer science, statistics, mathematics, data science or equivalentStrong background in machine learning (graphical models, Bayesian and neural networks), statistics, and preferably causal inference methodsKnowledge of and/or experience with time-series data, preferably clinical dataProgramming expertise in Python, R, and SQLInterest and/or experience in working with healthcare problems (particularly surgical procedures)Demonstrated skill in scientific writingExcellent interpersonal skills with the ability to work independently and in collaboration with a multidisciplinary team of surgeons and engineersExperience with healthcare data and building real-world systems is a plus
At the Helmholtz Zentrum München, you can contribute together with leading researchers to the investigation of the development, prevention and treatment of environmental diseases such as diabetes, chronic lung diseases and allergies. In order to further promote your professional development, we offer extensive and targeted research training and career programmes. We support the reconciliation between work and private life with flexible working time models, occupational health management, day care facility for children, a childcare subsidy, Elder Care, as well as other counseling and support services.
Remuneration and benefits are in accordance with the collective agreement for the public service (EG 13 50% TV EntgO Bund). In addition, there is also the possibility of granting an allowance amounting to 15% if the necessary conditions are fulfilled.
The position is (initially) limited to three years.
The activity involves special knowledge and experience specific to own scientific skills.
As a holder of the Total E-Quality Award, we promote equality of opportunity. Applications from women are welcome. Qualified applicants with physical disabilities will be given preference.
We are looking forward to receiving your comprehensive online application.