Postdoctoral Researcher / Research Associate in Machine Learning and NLP for health data
Postdoctoral Researcher / Research Associate in Machine Learning and NLP for health data
Postdoctoral Researcher / Research Associate in Machine Learning and NLP for health data
Postdoctoral Researcher / Research Associate in Machine Learning and NLP for health data
Universität Bern
Fach- und Hochschulen
Bern
- Art der Anstellung: Vollzeit
- 92.500 CHF – 122.500 CHF (von XING geschätzt)
- Vor Ort
- Zu den Ersten gehören
Postdoctoral Researcher / Research Associate in Machine Learning and NLP for health data
Über diesen Job
Postdoctoral Researcher / Research Associate in Machine Learning and NLP for health data
60-100%
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.
Tasks
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 and, eventually, patient outcomes.
Your work will contribute to building TrialSim, a digital platform that uses deep learning to analyze large-scale clinical trial data. TrialSim will integrate unstructured data from:
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:
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 and, eventually, patient outcomes.
Your work will contribute to building TrialSim, a digital platform that uses deep learning to analyze large-scale clinical trial data. TrialSim will integrate unstructured data from:
- Clinical trial registries and corresponding publications
- Electronic health records (EHRs) from Bern and international sources
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:
- Provide technical support for AI/NLP and machine learning projects within the group.
- Contribute to ongoing teaching efforts in the group/at the Department
- Contribute to publications and (inter)national conferences.
- Contribute to a positive and collaborative team culture.
Requirements
Required academic qualifications:
Minimum technical and professional requirements (all must be met):
1) Strong skills and applied expertisein machine learning/deep learning, including MLOps and MLflow, ideally in Python. At least minimal experience with NLP, particularly Transformers (e.g., Hugging Face, PyTorch), and/or open-source LLMs (e.g., BERT, LLaMA, Mistral), prompt engineering, fine-tuning, or agentic LLMs – or at least motivation to learn these skills
2) Exposure to biomedical or health data. A plus but not required: familiarity with EHR standards (e.g., FHIR, OMOP, HL7) and privacy frameworks (GDPR, HIPAA)
3) At least some experience in academic teaching
Additional expectations:
- PhD or MSc degree in computer science/informatics, medical data science, health informatics, statistics, mathematics, software engineering, or a related field.
Minimum technical and professional requirements (all must be met):
1) Strong skills and applied expertisein machine learning/deep learning, including MLOps and MLflow, ideally in Python. At least minimal experience with NLP, particularly Transformers (e.g., Hugging Face, PyTorch), and/or open-source LLMs (e.g., BERT, LLaMA, Mistral), prompt engineering, fine-tuning, or agentic LLMs – or at least motivation to learn these skills
2) Exposure to biomedical or health data. A plus but not required: familiarity with EHR standards (e.g., FHIR, OMOP, HL7) and privacy frameworks (GDPR, HIPAA)
3) At least some experience in academic teaching
Additional expectations:
- Enjoying to mentor and (technically) support students and more junior staff
- Strong team fit - you value collaboration, shared goals, and respectful communication
- Motivation for topics such as drug development, clinical trials, health data, and statistical modeling as well as interdisciplinary work at the intersection of medicine and computer science
- Willingness to publishing at least one peer-reviewed paper per year
We offer
- 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.
Contact
If you have any inquiries, please contact Prof. Ineichen Benjamin, at benjamin.ineichen@uzh.ch.
Are you interested? Then please send us your complete application to HR Administration
(hr.dcr@unibe.ch) by (August 31st, 2025), at the latest.
Required application documents:
Only complete applications will be considered. We will invite promising candidates for an interview.
(hr.dcr@unibe.ch) by (August 31st, 2025), at the latest.
Required application documents:
- Motivation letter explaining your interest in this particular project and environment
- CV, including publications
- Academic transcript/record of grades
Only complete applications will be considered. We will invite promising candidates for an interview.
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