Localização: Champalimaud Centre for The Unknown
Entidade Empregadora: Champalimaud Foundation
Regime: Tempo Inteiro
Posição: Pós-Doutoramento
Início da Candidatura: 06 Jul. 2026
The Champalimaud Foundation (Fundação D. Anna de Sommer Champalimaud e Dr. Carlos Montez Champalimaud), a private, non-profit research institution in Lisbon, Portugal, is looking for a Postdoctoral Researcher to join the Surgery, Care, Outcomes, Personalization, and Empowerment (SCOPE) Lab, within the Breast Cancer Research Programme.
The SCOPE Lab is led by Professor Maria João Cardoso (Head of the Breast Unit, Champalimaud Foundation). The postdoctoral researcher will be assisted in their scientific and technical work by Dr. João Santinha (PI and Co-Lead of the Digital Surgery Lab and member of the Breast Imaging Group) and Dr. Luís Elvas (Health Data Engineer of the Breast Cancer Research Programme).
The selected candidate will lead Champalimaud Foundation´s technical contributions to ResPECT (Representing People´s Experience of Cancer and its Treatment) - one Pan-European generative AI project in healthcare, funded with €16 million by the Horizon Europe programme (HORIZON-HLTH-2025-01-CARE-01). ResPECT brings together leading partners from over 10 countries to develop the first end-user–driven, trustworthy generative AI Virtual Assistant for patients, clinicians, and health systems in oncology and mental health care.
CF leads Work Package 3 (Generative AI agent and system architecture) of the project, with key responsibilities also across WP4 (Medical and Policy Assistants), WP5 (LLM output validation), and WP7 (legal and ethical analysis of AI-assisted decision support). The successful candidate will play a central role in shaping the technical architecture, leading the development of a multilingual, federated, explainable generative AI agent that captures patient narratives across physical, psychological, social, and financial burden domains and translates them into guideline-linked, clinically actionable recommendations.
The selected candidate will:
- Lead the design and development of the ResPECT generative AI agent, including a modular multi-agent architecture (clinical dialogue agent, retrieval/decision agent, guardrail agent, SOAP-note agent) following the g-AMIE paradigm with instruction- tuning, self-play simulated dialogues, and inference-time progressive reasoning.
- Adapt and fine-tune open-source European LLMs (e.g., Mistral, EuroLLM) for multilingual clinical narrative classification across burden domains, with intensity scoring, natural-language summaries, and calibrated confidence values.
- Build a hybrid retrieval-augmented generation pipeline (RAG-N for narrative classification, RAG-G for guideline-grounded recommendations) combining dense (FAISS) and sparse (BM25) retrieval, multilingual embeddings, and reranking.
- Design and implement the Knowledge Fusion Engine integrating PROMs, CTCAE events, lab values, and imaging descriptors via FHIR-compliant schemas to ground generation in clinical context.
- Develop explainability-by-design mechanisms (saliency maps, surrogate decision paths, evidence tracebacks), continuous safety monitoring (hallucination detection, bias and drift audits), and guardrail policies aligned with the EU AI Act.
- Deploy and orchestrate federated learning across European clinical sites (Flower, PySyft) to enable GDPR-compliant decentralised training while preserving data sovereignty.
- Develop the physician voice cloning module (VITS / YourTTS / F5-TTS) and emotion- and literacy-adaptive interaction modules for inclusive, trust-enhancing patient interactions.
- Expose REST/OpenAPI services and HL7 FHIR resources for downstream integration in the Medical Assistant (CANKADO) and Policy Assistant (UCAM / WHO-IARC).
- Coordinate technical activities with international consortium partners (TABC, CANKADO, Charité, KSE, UCAM, EORTC, NKI) and contribute to publications, conference presentations, and EU reporting.
- Co-supervise junior researchers and PhD students working on related topics within the SCOPE Lab and the Digital Surgery & Breast Imaging Lab.
Research Field: Computer science
Education Level: PhD or equivalent
- 1st degree in Computer Science, Electrical and Computer Engineering, Biomedical Engineering, Applied Mathematics, or a related field.
- PhD in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a closely related field, with a thesis focused on Large Language Models, agentic AI systems, medical NLP, or clinical decision support.
- Demonstrated research excellence through first-author/corresponding-author publications in top-tier venues - either machine learning conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, MICCAI, MIDL) or high-impact medical AI journals (Q1 top 5/10 and Q2).
- Strong programming skills in Python and the modern ML ecosystem (PyTorch, HuggingFace Transformers, PEFT, DeepSpeed / FSDP).
- Experience with multilingual NLP and adaptation of LLMs to specialised domains, ideally including healthcare.
- Familiarity with medical AI evaluation methodology - SPIRIT-AI, CONSORT-AI, TRIPOD-AI reporting standards; OSCE-style human evaluation; psychometric concepts relevant to PROM validation.
- Working knowledge of EU regulatory frameworks for medical AI - EU AI Act (high-risk medical AI), GDPR, MDR; awareness of ISO 14971, ISO 27001, ISO 13485, IEC 62304.
- Excellent collaborative and communication skills, with proven ability to work in interdisciplinary, multi-institutional teams.
Hands-on experience with modern LLM toolin
The candidate is expected to demonstrate hands-on experience across the following tool categories. Equivalent tooling experience accepted where the candidate demonstrates depth in the underlying concepts:
- Local inference and serving: vLLM, SGLang, Ollama
- Fine-tuning: Unsloth, TRL, Axolotl (LoRA / QLoRA, SFT, DPO / GRPO)
- Agent frameworks: LangGraph, DSPy, smolagents
- RAG infrastructure: FAISS + BM25, Qdrant, pgvector
- Federated learning: Flower, PySyft, or NVIDIA FLARE
- Evaluation and observability: LangFuse, Ragas, Inspect
- Guardrails and safety: NeMo Guardrails, Giskard
- Constrained generation and structured outputs: Outlines, Instructor, XGrammar
- Speech and voice: Parakeet, Whisper / Faster-Whisper, XTTS / F5-TTS
- Healthcare-specific tools: HAPI FHIR, MedSpaCy, Docling
Desirable skills that will also be considered
- Prior experience leading or contributing substantially to large multi-partner research projects (Horizon Europe, NIH, or equivalent).
- Experience with HL7 FHIR, openEHR, or other clinical interoperability standards in production settings.
- Experience with causal inference, counterfactual generation, or generalisability methods in medical AI.
- Open-source contributions to relevant libraries (HuggingFace, LangGraph, Flower, vLLM, or similar).
- Prior experience co-supervising PhD students or junior researchers.
- Working knowledge of Portuguese (not required; English is the working language of the lab and the consortium).
ENGLISH
Level: Excellent
- Competitive remuneration package commensurate with skills, qualifications, and experience.
- Full immersion into a research excellence ecosystem with highly motivated researchers, supported by state-of-the-art technology and continuous development opportunities.
- Access to CF`s high-performance computing infrastructure and clinical datasets from the Champalimaud Clinical Centre.
- Strong international collaboration with leading partners across 10+ European countries, including direct interaction with the two landmark Lancet Commissions on Cancer and Health Systems and on the Mental Health Effects of the COVID-19 Pandemic.
- Substantial opportunities for high-impact publications at top ML conferences and medical journals, conference travel, and visibility within the European generative AI healthcare landscape.
- Duration of the Fellowship: 24 months, automatically extended by 12 months subject to the candidate’s performance over the duration of the project (48 months).
Expected starting date: 1 September 2026 (with flexibility to be arranged with the selected candidate).
National, Foreign, or Stateless candidate(s) who hold a PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical and Computer Engineering, Biomedical Engineering, or other related scientific areas, and a scientific and professional curriculum that reveals a profile appropriate to the activity to be developed.
In case of PhD degrees awarded by foreign higher education institutions, the degree must comply with the provisions of Decree-Law No. 341/2007, of 12th of October, and all formalities established therein must be fulfilled by the deadline for application submission.
Candidates will be screened to determine eligibility based on minimum qualifications listed in the
call. Candidates meeting minimum requirements will be scored according to the required
application materials.
Relative weighting:
- CV 40%,
- Motivation letter 15%,
- Interview 45%.
If shortlisted, candidates` referees may be contacted and may count towards evaluation.
When tied, top candidates will be interviewed (in person, or remotely). The result of the interview is then the sole evaluation criterion (basic documentation required). If no tie, holding of interviews is at the discretion of the Evaluation Committee.
- Prof. Maria João Cardoso – Head of the Breast Unit and SCOPE Lab, Breast Cancer Research Programme, Champalimaud Foundation
- Dr. João Santinha – PI and Co-Lead, Digital Surgery & Breast Imaging Lab, Breast Cancer Research Programme, Champalimaud Foundation
- Dr. Luís Elvas – Health Data Engineer, Breast Cancer Research Programme, Champalimaud Foundation
- CV, letter of motivation indicating up to 3 references, and a portfolio of prior relevant projects (GitHub repository or equivalent) - submitted as a single annexed PDF document.
- Proof of qualifications (certificates) may be required at a later date for the formalisation of the contract agreement. Degrees obtained abroad may need to be formally recognised in Portugal.
Candidates must complete the standard application form and submit a single annexed PDF document containing the application materials stated in the call.
The call reference will be asked and not having one may invalidate your submission. This call reference is: Postdoc_SCOPE_ResPECT_2026.
Applications are also accepted in paper, sent or handed to the Fellows Support Office at the Champalimaud Centre for the Unknown, Avenida Brasília, 1400-038 Lisbon, Portugal.
All candidates will receive a receipt of acknowledgment within 7–10 business days. The highest scoring candidate will be notified by email or telephone. All other candidates will be notified solely by email of the final outcome of the recruitment process.
After being notified, candidates will have 10 working days to present a claim for redress by email ([email protected]). If no claim is received by the Champalimaud Foundation, the Evaluation Committee`s decision will become definitive.
Research is at the heart of the Champalimaud Centre for the Unknown, a modern research and clinical facility situated on the waterfront in Lisbon, Portugal. The goal of Champalimaud Research (CR) is to perform world-leading fundamental and translational research. Current research work is focused on the fields of neuroscience, physiology, and cancer. Champalimaud Research comprises a team of over 400 scientists from around the world. English is the official language of the Centre. Lisbon`s sunny Atlantic Mediterranean climate, vibrant culture, and high quality of life make this a great place to live and work.
The Surgery, Care, Outcomes, Personalization, and Empowerment (SCOPE) Lab, led by Professor Maria João Cardoso, sits within the Breast Cancer Research Programme of the Champalimaud Foundation. The lab bridges surgical oncology, patient-reported outcomes, personalised care pathways, and digital empowerment of patients and clinicians, working closely with the Digital Surgery & Breast Imaging Lab (co-led by Dr. João Santinha) on AI-driven research at the intersection of clinical decision support, generative modelling, and lifecycle evaluation of clinical AI systems.
In line with our community guidelines and Equality, Diversity and Inclusion policy and Gender Equality Plan, no candidate may be privileged, favored, prejudiced or deprived of any right or excluded from any duty on the basis of age, sex, gender identity, sexual orientation, race/ethnicity, disability, chronic illness, language, nationality, territory of origin, family status (marital, pregnancy and maternity, having or not having dependents), socioeconomic situation, education, religion, political or ideological beliefs, trade union membership, or on any other grounds which are irrelevant to decision-making.
The present call has the sole purpose of filling the indicated research position. This call may be canceled at any time on or before the jury`s evaluation of all eligible candidates and the recruitment process shall be concluded on the day of the full execution of the contract between the Champalimaud Foundation and the selected candidate.
