Department of Clinical Research
Start date preferentially as soon as possible but open for debate (latest spring 2026).
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 (https://stride-lab.pages.uzh.ch/website/), 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.

Developing drugs for clinical applications is challenging, with only about 5% of therapies receiving regulatory approval (Ineichen et al., PLoS Biology, 2024). While some failures are due to the complexity of innovative therapies, others stem from adjustable factors in drug testing, such as outcome measures, trial duration, and model selection (Berg et al., eBiomedicine, 2024). The impact of these factors is difficult to assess in individual trials but can be uncovered through large-scale clinical trial data analysis (Ineichen et al., Nature Reviews, 2024).

Our approach combines expertise in medicine, evidence synthesis, and natural language processing (NLP) (Doneva et al., EMNLP, 2024) with Bern's extensive clinical trial landscape and modern data science infrastructure. The goal is to identify the key factors driving successful drug approvals and use this knowledge to optimize clinical trial design. This shall be achieved by developing TrialSim, a digital platform that integrates deep learning to curate, integrate, and analyze large-scale animal and patient drug trial data.
Your PhD project will establish the preclinical arm of TrialSim by linking millions of animal drug studies to corresponding human trials. Our group has already built such a pipeline for neuroscience; your task is to refine and extend it across medical domains. A key subproject will focus on animal models of psychiatric diseases such as depression and schizophrenia: examining which outcome measures are used and how well they predict human results. The aim is to empirically determine which animal outcomes best predict successful translation to make animal testing more ethical.

The project has three components:

1. Systematic review – Map outcome measures commonly used in animal and clinical drug trials through a full systematic review, forming the evidence base for subsequent analyses.
2. Pipeline development – Refine and expand an existing data pipeline that retrieves, classifies, extracts, and aggregates information from literature databases, with real-time visualization in web applications.
3. Data-driven analysis – Use the curated dataset to identify animal outcomes that best predict successful human translation.

The project is fully data-driven; no animal or human studies will be conducted. Its results will contribute to more efficient and ethically optimized animal research by identifying where animal studies are most needed and reducing research waste.

During your PhD, you will gain expertise in systematic review methodology, data engineering for large-scale text and metadata pipelines, large language models and other NLP approaches (including agentic LLMs), and scientific writing. The project may include research visits to leading groups in the USA and UK. You will be enrolled in a structured PhD program (graduate school). Contribution to teaching and publication efforts are welcome.

We are looking for candidates with a high enthusiasm for this project, including for animal welfare, clinical trials, and data science, enjoying interdisciplinary work at the intersection of medicine and computer science.

Required academic qualifications:

• Master degree in computer science/informatics, medical data science, health informatics, statistics, mathematics, software engineering, or a related field.

Required technical qualifications:

• Expertise in Python programming and machine learning skills, including MLOps, MLflow and/or Docker.
• Experience in data engineering and building data pipelines.
• Exposure to health data like human or animal data.

Nice to have: Experience with transformer models (e.g., BERT) or generative LLMs for data curation and extraction.
Additionally required soft skills:

• Excellent organizational and planning skills.
• Strong team spirit: It is absolutely central for us that you fit well into our team.

• Purposeful work aimed at improving animal welfare and patient care and advancing treatment for neurological (and other) diseases.
• A 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 (see digitalization strategy).
• Collaboration within Switzerland's largest medical faculty and hospital complex, offering extensive networking opportunities; but also international collaboration.

Prof. Benjamin Ineichen E-Mail schreiben

Send your application or any questions until December 12, 2025: E-Mail schreiben
Required application documents:

• Motivation letter explaining your interest in this particular project and environment
• CV, including publications
• Academic transcript/record of grades

Note: Only complete applications will be considered. We will invite promising candidates for an interview.