You will join the CaRe-NLP: Human-Centric and Responsible NLP methods for Dutch healthcare project. CaRe-NLP's main goal is to develop human-centric and responsible NLP and ML methods for healthcare in the Netherlands, Europe, and worldwide. CaRe-NLP's focus is methodological, meaning you will focus on developing, building, and testing state-of-the-art large language models (LLMs) that are interpretable and explainable, ensure privacy and fairness, prevent bias, and cope with data scarcity.

This PhD project will be co-supervised by dr. Iacer Calixto and prof. Ameen Abu-Hanna at the University of Amsterdam and together with prof. Barbara Plank from the MaiNLP research lab at LMU Munich.

You will tackle relevant clinical problems and develop methods for non-English (including Dutch) multi-modal electronic heath records (EHRs) data that include (combinations of) free-text clinical notes collected in primary and/or secondary settings (including intensive care); medical images; time series measurements; medical knowledge graphs.

We currently offer the PhD position titled PhD-quantifying-disagreement-and-uncertainty where you will be responsible for developing, building and testing clinical LLMs that model uncertainty and disagreement in annotations (input) and predictions (output). You will develop solutions and apply them to learning from patient electronic health records (EHRs) and free text.

Furthermore, you will:

• Liaise with a network of clinical partners of the CaRe-NLP project and will tackle relevant healthcare problems using rich real-world clinical data;
• Have access to excellent HPC facilities and strong support to publish the results of your work in the best scientific conferences and journals in NLP, ML, (medical) AI, and healthcare in general;
• Contribute to the mentoring of master's students on topics related to your research.

You have:

• A MSc degree in computer science, natural language processing, machine learning, artificial intelligence, statistics, or a related discipline;
• A strong technical background and experience with natural language processing and machine learning methods, toolkits, and best practices;
• Experience with or interest in learning about pretraining and adapting LLMs using large datasets and/or uncertainty quantification with neural networks and generative models (e.g. LLMs, VAEs, GANs, diffusion models);
• Programming skills in Python and ML libraries (PyTorch);
• A responsible, independent and resilient mindset, and you speak English fluently.

Furthermore, these are required but helpful:

• You have experience with data processing pipelines and with pre-training/fine-tuning/deploying Large Language Models in HPC;
• You contributed to open-source projects;
• You have experience with clinical data and related application areas (e.g., cancer, primary/intensive care);
• You have knowledge of the Dutch language, since much of the data you will work with includes free-text clinical notes in Dutch.

• A stimulating and inclusive research environment with ample opportunity for collaboration.
• You will be employed by Amsterdam UMC Research BV.
• A contract for 12 months with the prospect of extension for 4 years upon satisfactory performance.
• Salary scale OIO: € 3.017 to € 3.824 gross per month based on full-time employment (depending on education and experience) and a year-end bonus of 8.3%. Calculate your net salary here.
• Holiday hours: 186,4 per year for fulltime and a possibility to save additional hours.
• Pension accrual with BeFrank, a modern, comprehensible and fairly priced pension.
• For >7 km each way, 100% reimbursement for public transport travel costs and, for private transport, €0.18 per km up to a maximum of 40 km each way.
• Do you prefer walking or cycling? Take advantage of our good bike scheme. Moreover, you will receive a reimbursement of €0.18 per km.

Watch this video with more information about joining Amsterdam UMC Research BV.

Amsterdam UMC

You will be appointed at the Department of Medical Informatics at the Amsterdam UMC, University of Amsterdam. As part of your PhD trajectory, you will have the opportunity to visit the Munich AI and NLP (MaiNLP) research lab at LMU Munich led by prof. Barbara Plank. Moreover, you will also have the opportunity to collaborate with colleagues within the University of Amsterdam in the departments of primary care and intensive care at Amsterdam UMC, at the Informatics Institute (IvI-UvA), the Institute for Logic, Language and Computation (ILLC-UvA) and the Data Science Center (DSC-UvA).

You will be part of the NLP4Health Lab and will be supervised by dr. Iacer Calixto, prof. dr. Barbara Plank, and prof. dr. Ameen Abu-Hanna. Dr. Calixto is Assistant Professor in Artificial Intelligence and works on machine learning (ML) for vision and language, natural language processing, and artificial intelligence (AI) in healthcare. Prof. Barbara Plank is Chair of Computational Linguistics at LMU Munich and head of the Munich AI and NLP (MaiNLP) research lab, with a research focus on trustworthy human-centric NLP, including handling of uncertainty and learning from scarce data. Prof. Abu-Hanna is a Principal Investigator in Methodology in Medical Informatics and has extensive experience in AI, ML, and prognostic modelling and evaluation.

During the publication period, applications will be handled continuously. If the vacancy is filled, it will be closed prematurely.

Note: by applying for this position, your cv and motivation letter will be shared with prof. Barbara Plank at LMU Munich.

The intended start date is ideally between 1 March and 15 April 2025, or as soon as possible thereafter.

If you have any questions about this position, please feel free to contact Iacer Calixto, via i.coimbra@amsterdamumc.nl.

For more information about the application procedure, please contact Rhiannon Sandfort, Recruitment advisor, via r.e.sandfort@amsterdamumc.nl.

A reference check, screening and hiring test may be part of the procedure. Read here whether that applies to you. If you join us, we ask you for a VOG (Certificate of Good Conduct).

Internal candidates will be given priority over external candidates in case of equal suitability.

Acquisition in response to this vacancy is not appreciated.