Habtamu Abebe Getahun
2 months ago
PhD Position in Medical Data Science, NLP, and Animal-to-Human Translation at University of Bern University of Bern in Switzerland
Degree Level
PhD
Field of study
Pharmacology
Funding
The position is fully funded (100%). The project offers flexible working hours, opportunities for first- and co-authorships on peer-reviewed scientific articles, access to a dynamic machine learning community, and potential research visits to leading groups in the USA and UK. No specific stipend amount or tuition coverage is mentioned.
Deadline
Dec 12, 2026
Country
Switzerland
University
University of Bern

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About this position
The University of Bern is offering a PhD position in Medical Data Science within the STRIDE-Lab at the Department of Clinical Research. This opportunity focuses on the development of TrialSim, an AI-driven platform designed to improve the translation of findings from animal studies to human clinical trials. The PhD project will specifically establish the preclinical arm of TrialSim by linking large-scale animal study data with corresponding clinical trials, with a particular emphasis on psychiatric diseases such as depression and schizophrenia.
The STRIDE-Lab, led by Assistant Professor Benjamin Ineichen, is a multidisciplinary group with expertise in medicine, neuroscience, statistics, and computer science. The lab aims to bridge the gap between preclinical and clinical research, advancing therapy development for human diseases and improving animal welfare. The project is fully data-driven, involving systematic reviews, pipeline development for data extraction and aggregation, and data-driven analysis to identify animal outcomes that best predict successful human translation. No animal or human studies will be conducted directly.
Applicants should have a Master’s degree in computer science, medical data science, health informatics, statistics, mathematics, software engineering, or a related field. Required skills include Python programming, machine learning (including MLOps, MLflow and/or Docker), data engineering, and experience with health data. Experience with transformer models or generative LLMs is a plus. Strong organizational skills and team spirit are essential.
The position is fully funded and offers flexible working hours, opportunities for authorship, access to a vibrant machine learning community, and potential research visits to leading groups in the USA and UK. The University of Bern provides a supportive, inclusive environment and is located in Switzerland’s capital, offering rich cultural and natural experiences.
To apply, submit a motivation letter, CV (including publications), and academic transcript via the University of Bern job portal or by email to [email protected] by December 12, 2025. For questions, contact Prof. Benjamin Ineichen at [email protected].
Funding details
The position is fully funded (100%). The project offers flexible working hours, opportunities for first- and co-authorships on peer-reviewed scientific articles, access to a dynamic machine learning community, and potential research visits to leading groups in the USA and UK. No specific stipend amount or tuition coverage is mentioned.
What's required
Applicants must hold a Master's degree in computer science, informatics, medical data science, health informatics, statistics, mathematics, software engineering, or a related field. Required technical qualifications include expertise in Python programming, machine learning skills (including MLOps, MLflow and/or Docker), and experience in data engineering and building data pipelines. Exposure to health data (human or animal) is necessary. Experience with transformer models (e.g., BERT) or generative LLMs for data curation and extraction is a plus. Excellent organizational and planning skills and strong team spirit are essential.
How to apply
Submit your application via the University of Bern job portal or send your application to [email protected]. Include a motivation letter, CV (with publications), and academic transcript. Only complete applications will be considered. Contact Prof. Benjamin Ineichen for questions.
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