🚀 Doctoral student (m/f/d) / Research Associate (m/f/d) in the field of Statistics / Data Science

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Justus-Liebig-Universität Gießen

💰 Earn $40.000 – $60.000 / year
  • 📍 Location: Gießen
  • 📅 Posted: Oct 17, 2025

Overview

Founded in 1607, Justus Liebig University Giessen (JLU) is a research university rich in tradition. Inspired by curiosity about the unknown, we enable around 25,000 students and 5,800 employees to advance science for society. Join us in breaking new ground and writing success stories - your own and those of our university.

Support us from next possible date in full-time as a

Doctoral student (m/f/d) / Research Associate (m/f/d) in the field of Statistics / Data Science

The position is part of the externally funded project “PVRI GoDeep Global Meta-Registry for Pulmonary Hypertension" and is to be filled on a fixed-term basis in accordance with § 2 WissZeitVG and § 72 HessHG with the opportunity for own academic qualification at the Chair of Pulmonary Hypertension, Department of Internal Medicine at the Faculty of Medicine. The salary is in accordance with the collective labour agreement of the State of Hessen (E 13 TV-H).

As long as the maximum permissible duration of a fixed-term contract is not exceeded, you will be employed for a period of 3 years.

Your tasks

The project aims to build a global collection of clinical research data for patients suffering from pulmonary hypertension (PH). With over 40,000 enrollees so far, our GoDeep registry is the world’s largest collection of PH patients. As a research collaboration, we work together with leading physicians across the world e.g. from Stanford (USA), Johns Hopkins (USA), Imperial College London (UK), Johannesburg (South Africa), Abu Dhabi (UAE), Sydney (Australia) etc. With this unprecedented amount of data and a team of highly motivated physicians and data scientists, we are addressing current and upcoming challenges in PH research.

Your tasks involve

  • Data preparation and quality checks
  • Statistical modelling and analysis (in cooperation with physicians)
  • Developing novel methods for working with heterogeneous large data collections

The provision of academic services (including the processing of a research project financed by third party funds on a temporary basis) also serves the purpose of academic qualification.

Qualifications and competences

  • Completed Master‘s degree or equivalent university degree in Statistics/ Data Science/ Epidemiology or a related field
  • Experience in using R for data analysis
  • Sound knowledge and understanding of statistical methods such as multiple imputation, general(ized) linear models, survival models, dimension reduction, and clustering
  • Strong interest in working with real patient data and in collaborating with leading scientists
  • Ability to work in a team, communication skills and very good written and spoken English. Basic knowledge of German is an advantage.

Our offer to you

  • A varied job with flexible working hours
  • Free use of local public transport (LandesTicket Hessen)
  • More than 100 training seminars, workshops and e-learning opportunities per year for personal development, as well as a wide range of health and sports activities
  • Remuneration according to TV-H, company pension scheme, child allowance and special payments
  • Good compatibility of family and career (certificate "audit familiengerechte hochschule")

JLU welcomes qualified applications regardless of biological and social gender, disability, nationality, ethnic and social origin, religion, ideology, age as well as sexual orientation and identity. The JLU aims for a higher proportion of women in accordance with the women’s promotion plan. We therefore particularly encourage qualified female candidates to apply. JLU is certified as a family-friendly university. Applications from disabled people of equal aptitude will be given preference.

You want to break new ground with us?

Apply via our online form by October 31, 2025 , indicating reference number 500/11. We look forward to receiving your application.

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