Description du poste
Le Département de Recherche Clinique de l’Université de Berne offre un environnement de recherche de qualité. Rejoignez une équipe innovante axée sur la recherche centrée sur le patient.TâchesDévelopper des applications cliniques utilisant des données médicales.Concevoir des architectures de données pour le traitement clinique.Encadrer des doctorants et collaborer sur des publications.CompétencesDoctorat en science des données médicales ou domaine connexe requis.Expertise en gestion de données cliniques sensibles.Connaissance des réglementations de confidentialité comme le GDPR.Department of Clinical ResearchEmployment upon agreementTheFaculty of Medicine at the University of Bernis 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 anddigitalization, 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 theSTRIDE-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.TasksDeveloping 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.Your work will contribute to kicking off the development of TrialSim, a digital platform that integrates multimodal deep learning to analyse large-scale clinical trial data from two sources: Clinical trial registries and Electronic health record (EHR) data from trials in Bern and international sources. TrialSim should combine imaging and textual data while addressing modern privacy requirements in clinical research. It will lay the fundament to understanding factors that drive successful approval by applying advanced statistical modeling and machine learning to analyze outcome measures, adverse events, and trial design, identifying what contributes to drug approval.Your specific tasks include:1. Establish secure, compliant EHR data pipelines in collaboration with hospital IT systems (FHIR, OMOP, HL7).2. Oversee data privacy, safety, and regulatory compliance (GDPR, HIPAA).3. Design and implement data architecture that ensures scalable and privacy-preserving processing of sensitive clinical data.4. Develop and apply data anonymization techniques such as federated learning to ensure compliance.5. Mentor PhD students and contribute to system-level design for TrialSim’s integration of text, imaging, and structured data streams.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 NLP and machine learning projects within the group.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 conferences.Foster a positive and collaborative team culture.RequirementsWe are looking for candidates with a high enthusiasm for the projects we work on, including for drug development, clinical trials, health data, and statistical modelling, enjoying interdisciplinary work at the intersection of medicine and computer science.Academic qualifications:PhD or MSc degree in medical data science, health informatics, computer science/informatics, statistics, software engineering, or a related field.Required technical qualifications:Expertise in handling sensitive clinical data (e.g., EHRs, FHIR, OMOP, HL7).In-depth knowledge of privacy regulations (GDPR, HIPAA) and compliance protocols.Experience in privacy-preserving techniques, e.g. de-identification, and federated learning techniques.Proficiency in data architecture design for large-scale, privacy-preserving clinical data systems.Experience working with hospital IT systems and data governance processes.Proficiency in Python and/or R and common ML frameworks and tools, libraries, data structures, and data modeling.A proven publication record with at least one first author publication in peer-reviewed international journals.Preferred but not required: Experience with developing Transformers-based NLP models using ML frameworks like Hugging Face and PyTorch using health-related data and/or 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.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 yearWe offerPurposeful 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.ContactIf you haveany inquiries, please contact Prof. Ineichen Benjamin, atE-Mail schreiben.Are you interested?Then please send us yourcompleteapplication to HR Administration(E-Mail schreiben) by (June 15th, 2025), at the latest.Required application documents:CV, including publicationsMotivation letter explaining your interest in this particular project and environmentAcademic transcript/record of gradesNote:Only complete applications will be considered. We will invite promising candidates for an interview. Lire la suite
