Postdoc TAM (Machine Learning) Part-time

Postdoctoral Researcher In Machine Learning

Join a global team that is shaping the future of data-driven clinical development. Biostatistics and Data Sciences (BDS) is a worldwide organization within the Human Pharma Business Unit. In close collaboration with internal functions and external partners, we combine deep methodological and technical data-science expertise with scientific and business insights to enable smart, timely and confident decision-making across all stages of drug development, approval and patient access.

As we continue transforming BDS into a modern data science organization capable of thriving in a rapidly evolving environment, we are expanding our capabilities to fully unlock the value of our clinical data. To support this journey, we are seeking a Postdoctoral Researcher who will advance innovative Machine Learning methodologies with in the field of patient-specific clinical or real-world data and digital biomarkers.

This position is part time eligible with 80%. This Position is limited for 2 years. Note: To make it easier to find our job advertisements, we use the usual designation “postdoc”. Of course, this advertisement is not only addressed to applicants directly after completing their doctorate, but to all qualified candidates.

Ready to shape the next generation of clinical data science? Apply now and join us on this exciting journey.

Tasks & Responsibilities

In your new role you will:

• Identify, implement, and evaluate innovative methods in areas such as multivariate statistical prediction models, supervised and unsupervised learning, model assessment and explainable AI, optimized feature engineering, and deep learning approaches for signal processing and computer vision
• Translate your research into ready-to-use software, graphical user interfaces, technical reports, and scientific publications in close collaboration with statistics colleagues within and outside BI
• Supervise bachelor's and master's students on relevant subtopics, guiding them toward high-quality internship reports, theses, and associated software outputs
• Actively build and maintain networks with academic institutions to foster collaboration and exchange of expertise
• Present your findings at internal and external meetings and contribute to the scientific community through publications in peer-reviewed journals
• Provide training on basic and advanced statistical learning methods to both statistics and non-statistics colleagues across BI
• Collaborate closely with internal experts and play an integral role within the Statistics team, contributing to cutting-edge data science initiatives across the organization

Requirements

We are looking for a candidate with:

• PhD in Mathematics, Statistics, or a related quantitative field, supported by a strong scientific foundation
• Proven expertise in both classical machine-learning techniques (e.g., penalized regression, support vector machines, tree-based methods) and advanced deep-learning methodologies
• Extensive experience in developing end-to-end machine-learning and deep-learning pipelines, from data preparation to model deployment
• Knowledge of generative modeling or Large Language Models, as well as familiarity with clinical development processes, is considered an advantage
• Strong proficiency in R, ideally including experience with R Shiny, R Markdown, Quarto, and the development or publication of R packages
• Solid programming skills in Python, including the use of core data-science and deep-learning libraries such as NumPy, pandas, scikit-learn, Keras, TensorFlow, and PyTorch; knowledge of IT and high-performance computing is an advantage
• Demonstrated track record of publishing scientific work, along with fluency in written and spoken English and strong interpersonal skills for effective collaboration with internal and external stakeholders