Ph.D position in Multimodal Large Language Models for Patient-Generated Data in Shared Decision Making (DC7)

There is a vacancy for a PhD Research Fellow in Multimodal Large Language Models for Patient-Generated Data in Shared Decision Making (DC7)  at Adhera Health SL (Spain) and the University of Bergen, Norway. The position is funded by the European Commission through the MSCA Doctoral Network Endotrain (Grant No: 101227148) and coordinated by the University of Bergen, Norway.

Supervisors: Dr. Luis Fernandez-Luque, Dr. Ana Moro (Adhera Health SL); Prof. Helge Ræder, Prof. Eystein Husebye (UiB)

Location: Spain

Duration: 3 years

Start date: August 2026 at latest

The students will be enrolled in the structured PhD programme at the University of Bergen.

This position is part of:

19 PhD Fellowships available in Digital Endocrinology in the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN).

Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and clinical medicine to transform diagnosis and treatment of adrenal diseases. 

Digital medicine is entering a new era where human “digital twins” and sensor-based monitoring allow personalised diagnosis and treatment. ENDOTRAIN will 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 focuses on adrenal disorders as a case study for advancing digital health in Europe.

About the PhD position:

This project is part of Work Package 2 – Technologies for Multimodal data of ENDOTRAIN and will advance the development of multimodal Large Language Models (LLMs) for integrating time-series physiological data, clinical assessments, and natural language summaries in the context of endocrine disorders, with a focus on adrenal insufficiency.

The project will:

• Design and evaluate multimodal AI architectures that combine wearable-derived time-series signals, structured clinical data, and patient-generated language reports.
• Explore advanced fusion strategies for integrating heterogeneous modalities into context-aware clinical decision support systems.
• Create visual and language-based representations of complex patient data to support shared decision making (SDM) between patients, caregivers, and clinicians.
• Conduct participatory research with ~40 patients living with adrenal insufficiency using surveys, interviews, and workshops.
• Produce publishable contributions at the intersection of AI, Human-Computer Interaction, and Digital Health, with targeted venues including AAAI, ACL, NeurIPS, ICML, ACM CHI, and ICHI.
• Contribute to ethical, explainable, and responsible deployment of multimodal LLMs in the management of endocrine diseases.

Research Fields: 

Artificial Intelligence, Multimodal Machine Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology

Secondments (Preliminary Plan): 

• UiB (Norway): 1–2 months — Patient and caregiver interviews, exploration of legal/ethical aspects, and integration of multimodal datasets.
• Karolinska Institutet (Sweden): 1 month — Development and evaluation of tailored self-reporting and visualization interfaces for endocrine patients.
• University Hospital Zurich (Switzerland): 1 month — Advanced analysis of patient-reported outcomes and integration with multimodal AI approaches.

Required:

• MSc (or equivalent) in Mathematics, Computer Science, Computational Linguistics, Engineering, or Medical Engineering
• Strong expertise in Artificial Intelligence, Machine Learning, and Natural Language Processing
• Experience with multimodal learning (integration of time-series, clinical, and textual data)
• Strong programming skills (Python, PyTorch/TensorFlow)
• Demonstrated coding portfolio: GitHub link required
• Excellent written and spoken English (working language of the project)
• Compliance with MSCA mobility rule: 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:

• Experience with LLMs, multimodal fusion, or explainable AI
• Background in digital health or clinical datasets, ideally related to endocrine diseases
• Record of publications or strong aspiration to publish in top-tier AI or HCI venues (AAAI, ACL, NeurIPS, ICML, ACM CHI, ICHI)
• Interest in responsible AI and patient-centered healthcare innovation

For further details, please visit our webpages: Multimodal Large Language Models for Patient-Generated Data in Shared Decision Making (DC7)  and the programme webpage: Endotrain Doctoral Training Network | UiB

• Competitive salary according to MSCA / national regulations
• Full social security coverage in host country
• Dedicated budget for travel, training, and secondments
• Excellent opportunities for international networking, cross-sector collaboration, and career development

All applications must be submitted via the Jobbnorge portal 

Your application must include the mandatory attachments from the ENDOTRAIN webpages – including “1. Application form” – “2. CV” – “3. Mobility declaration” – “4. Motivation Letter”

Eligibility (MSCA Doctoral Network rules)

• Applicants can be of any nationality.
• Must not have resided or carried out a main activity in the host country for more than 12 months in the past 36 months before start of the PhD.
• Must hold a master’s degree (or equivalent) enabling doctoral studies.
• Must not already hold a doctoral degree.
• English proficiency required – transcripts of diplomas in English must be uploaded in Jobbnorge.

In the “Jobbnorge-CV”: Only fill in the basics of your 1) personal details, 2) short information about your degrees (in the field “Education / academic qualifications”) – in order to minimise repetition with the CV attachment.

It is a condition of employment that the master’s degree has been awarded (documentation must be provided). If you have not yet completed your master’s degree, you can apply provided completion of the final master exam before the position start date. Please submit a statement from your institution confirming the expected date of award of your master’s degree.

For HR related questions contact the host institutions for the position you are applying