Luis Fernandez-Luque
3 weeks ago
PhD Research Fellow in Multimodal Large Language Models for Patient-Generated Data in Shared Decision Making (ENDOTRAIN DC7) University of Bergen in Norway
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Feb 15, 2026
Country
Norway
University
University of Bergen

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Where to contact
Official Email
[email protected]
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About this position
The University of Bergen, in partnership with Adhera Health SL (Spain), invites applications for a PhD Research Fellow position in Multimodal Large Language Models for Patient-Generated Data in Shared Decision Making, as part of the prestigious Marie Skłodowska-Curie Doctoral Network ENDOTRAIN. This three-year, fully funded position is based in Spain and coordinated by the University of Bergen, Norway, with a start date no later than August 2026.
ENDOTRAIN is Europe’s first doctoral network in digital endocrinology, integrating artificial intelligence, sensor technology, omics, and clinical medicine to revolutionize the diagnosis and treatment of adrenal diseases. The network aims to train a new generation of interdisciplinary experts who merge clinical endocrinology, AI, data science, engineering, ethics, and law into an integrated field of digital endocrinology. The programme uses adrenal disorders as a case study to advance digital health across Europe.
This PhD project is part of Work Package 2 – Technologies for Multimodal Data. The research will focus on developing and evaluating multimodal AI architectures that integrate wearable-derived time-series signals, structured clinical data, and patient-generated language reports. The goal is to create context-aware clinical decision support systems and visual/language-based representations of complex patient data to facilitate shared decision making among patients, caregivers, and clinicians. The project includes participatory research with approximately 40 patients living with adrenal insufficiency, using surveys, interviews, and workshops. Expected outcomes include publishable contributions at the intersection of AI, Human-Computer Interaction, and Digital Health, targeting top venues such as AAAI, ACL, NeurIPS, ICML, ACM CHI, and ICHI. Ethical, explainable, and responsible deployment of multimodal LLMs in endocrine disease management is a key focus.
Secondments are planned at leading European institutions: 1–2 months at UiB (Norway) for patient and caregiver interviews and legal/ethical exploration; 1 month at Karolinska Institutet (Sweden) for interface development and evaluation; and 1 month at University Hospital Zurich (Switzerland) for advanced analysis of patient-reported outcomes and multimodal AI integration.
Applicants must hold an MSc (or equivalent) in Mathematics, Computer Science, Computational Linguistics, Engineering, or Medical Engineering, with strong expertise in AI, machine learning, and NLP. Experience with multimodal learning and strong programming skills (Python, PyTorch/TensorFlow) are required, along with a demonstrated coding portfolio (GitHub link). Excellent English proficiency is essential. Compliance with the MSCA mobility rule is mandatory: applicants must not have resided or carried out their main activity in Spain for more than 12 months in the last 3 years before recruitment. Desirable qualifications include experience with LLMs, multimodal fusion, or explainable AI, background in digital health or clinical datasets (ideally related to endocrine diseases), publication record or strong aspiration to publish in top-tier AI or HCI venues, and interest in responsible AI and patient-centered healthcare innovation.
The position offers a competitive salary according to MSCA and national regulations, full social security coverage in the host country, and a dedicated budget for travel, training, and secondments. The programme provides excellent opportunities for international networking, cross-sector collaboration, and career development.
Applications must be submitted via the Jobbnorge portal, including all mandatory attachments from the ENDOTRAIN webpages: application form, CV, mobility declaration, and motivation letter. Eligibility requirements include holding a master’s degree (or equivalent) enabling doctoral studies, not already holding a doctoral degree, and English proficiency (transcripts of diplomas in English must be uploaded). If the master’s degree is pending, a statement from the institution confirming the expected award date must be provided.
Diversity and inclusion are core values of the MSCA ENDOTRAIN network, with a gender equality plan and encouragement for women, people with immigrant backgrounds, and people with disabilities to apply. For further details, visit the programme webpage or contact Dr. Luis Fernandez-Luque at Adhera Health SL.
Funding details
Available
What's required
Applicants must hold an MSc (or equivalent) in Mathematics, Computer Science, Computational Linguistics, Engineering, or Medical Engineering. Strong expertise in Artificial Intelligence, Machine Learning, and Natural Language Processing is required, along with experience in multimodal learning (integration of time-series, clinical, and textual data) and strong programming skills (Python, PyTorch/TensorFlow). A demonstrated coding portfolio (GitHub link) is mandatory. Excellent written and spoken English is required. Applicants must comply with the MSCA mobility rule: not have resided or carried out their main activity in Spain for more than 12 months in the last 3 years before recruitment. Desirable qualifications include experience with LLMs, multimodal fusion, or explainable AI, background in digital health or clinical datasets (ideally related to endocrine diseases), publication record or strong aspiration to publish in top-tier AI or HCI venues, and interest in responsible AI and patient-centered healthcare innovation.
How to apply
Submit your application via the Jobbnorge portal. Attach all mandatory documents from the ENDOTRAIN webpages, including the application form, CV, mobility declaration, and motivation letter. Ensure you meet the eligibility criteria and provide documentation of your master's degree. If your degree is pending, include a statement from your institution confirming the expected award date.
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