PhD Position in Clinical Data Engineering and Privacy-Preserving Machine Learning for Drug Development
100%
Department of Clinical Research
Employment upon agreement
The Faculty of Medicine at the University of Bern is an environment for high-quality, future-oriented research. Strong connections between basic research, engineering sciences, and university hospitals enable a unique setting for translational and patient-centered clinical research. The faculty prioritizes cross-disciplinary research and digitalization, fostering innovation in medical science. It is one of the largest medical faculties in Switzerland and is affiliated with the country's largest hospital complex.
The Department of Clinical Research (DCR) is a joint initiative of the University of Bern's Faculty of Medicine and its university hospitals, including Inselspital and the University Psychiatric Services (UPD). It supports and professionalizes clinical and translational research collaborations.
Our specialized divisions assist researchers throughout the entire research process, from project conception to result dissemination. We provide tailored educational programs and events on all aspects of clinical research, equipping researchers and students with the skills to conduct efficient and impactful studies. Our mission prioritizes patient-centered research, ensuring that patient perspectives are integral to our work.
The Medical Data Science group, led by Assistant Professor Benjamin Ineichen, a medical doctor with a PhD in neuroscience/pharmacology, is part of the DCR at the University of Bern. The group, known as the STRIDE-Lab, is a multidisciplinary team with expertise in medicine, neuroscience, statistics, and computer science. It focuses on bridging the gap between preclinical and clinical research and eventually drug approval, to advance therapy development for human diseases, with a focus on neuroscience. Using evidence synthesis and data science, the lab aims to improve experimental animal welfare while also contributing to better patient treatments.
For your PhD project, you will build the clinical trial arm of the TrialSlim platform: a secure and scalable clinical data pipeline. Your main tasks include:
1. Establish privacy-compliant electronic health records (HER) data pipelines in collaboration with hospital IT systems. For this, we will leverage the modern data infrastructure in Bern.
2. Design data access, cleaning, and structured data integration workflows using (multimodal) LLMs.
3. Develop and apply data anonymization such as federated learning techniques.
4. Integrate curated data into an agentic LLM framework and apply machine learning or deep learning to identify factors associated with successful drug development.
You will work at the interface of medicine and computer science, leveraging the large volume of clinical data available in Bern as well as from publications. Additionally, you will:
- Collaborate with researchers in related fields, such as computer vision, benefiting from the University of Bern's strong digital research environment (see digitalization strategy).
- Contribute to publications and (inter)national conferences.
- Foster a positive and collaborative team culture.
Required academic qualifications:
- Master's degree in computer science/informatics, machine learning, computational linguistics, statistics, software engineering, or a related field.
Required technical qualifications:
- Proficiency in Python and/or R and common ML frameworks and tools, libraries, data structures, and data modeling.
- Experience with privacy-preserving techniques, including data anonymization techniques such as federated learning for training on sensitive health data.
- Experience developing Transformers-based NLP models using ML frameworks like Hugging Face and PyTorch using health-related data.
- Experience working with open-source large language models (LLMs) (e.g. BERT, LLaMA, Mistral) : prompt engineering, (parameter efficient) fine-tuning and multimodal model developing to incorporate tabular and/or image (and potentially other) data.
- Skilled in MLOps, including model deployment, scaling, and monitoring using tools like Docker and MLflow. Experienced in optimizing and serving large language models (LLMs) using techniques like model quantization and efficient inference.
- Preferred but not required: Experience with agentic LLM systems. If not already acquired, it is expected that the participant develops this skill during the project.
Additional required soft skills:
- Excellent organizational and supervisory skills, you can work in a team as well as independently.
- Strong ability to multi-task, to meet timelines, to work accurately; you are stress resistant.
- The ambition to write and publish at least one peer-reviewed publication per year
- Purposeful work aimed at improving animal welfare and advancing treatment for neurological (and other) diseases.
- A small multidisciplinary team with expertise in medicine, neuroscience, statistics, and computer science.
- Flexible working hours.
- Opportunities for first- and co-authorships on peer-reviewed scientific articles whenever possible.
- Access to a dynamic machine learning community at the University of Bern, with a strong emphasis on digitalization.
- Collaboration within Switzerland's largest medical faculty and hospital complex, offering extensive networking opportunities.
- Bern, the capital of Switzerland, is a lively city with rich cultural offerings and easy access to Switzerland's most stunning natural landscapes.
- We are committed to diversity and inclusion, valuing different perspectives to drive innovation. We welcome applicants from all backgrounds and ensure a respectful, supportive environment where everyone can thrive.
(hr.dcr@unibe.ch) by (June 30th, 2025), at the latest.
Required application documents:
- CV, including publications
- Motivation letter explaining your interest in this particular project and environment
- Academic transcript/record of grades
Note: Only complete applications will be considered. We will invite promising candidates for an interview.
