PhD Candidate in AI-driven 3D spheroid modelling for lymphoma research

Descrizione

PhD Candidate in AI-driven 3D spheroid modelling for lymphoma research Sintesi in italiano (traduzione automatica): Un'organizzazione internazionale cerca un Dottorando in biologia computazionale per un progetto di ricerca sul cancro presso l'Amsterdam UMC e l'Università di Amsterdam. Il candidato lavorerà su modelli computazionali 3D per studiare l'interazione delle cellule tumorali con il microambiente e testare l'efficacia dei farmaci. Le mansioni includono lo sviluppo di modelli meccanicistici, l'analisi di dati sperimentali e la presentazione dei risultati in conferenze. È richiesta una laurea magistrale in scienze computazionali, scienze biomediche o ingegneria biomedica, con interesse per la biologia del cancro. È preferibile avere esperienza di programmazione in Python e una buona padronanza dell'inglese. Shape the future of cancer research with AI-Driven 3D spheroid model simulations! Join us! We are seeking a motivated PhD candidate to join our interdisciplinary team bridging experimental in vitro work, clinical data and AI-driven modelling for cancer research! In this role, you will bridge the gap between machine learning, computational biology, and haematological oncology. You do not need to arrive as an expert in modelling: what matters most is your passion for human physiology, your curiosity about cancer cells behaviour, and your willingness to develop new computational skills along the way. This position is genuinely interdisciplinary: you will collaborate daily with experimental biologists and clinicians at Amsterdam UMC, and computational biologist/AI experts at the University of Amsterdam (UvA). The heart of this project is to understand how tumor cells interact with their microenvironment, how drugs disrupt those interactions, and answering those questions by developing computational tools. This is what you will do Develop AI-driven mechanistic computational models of Non-Hodgkin lymphomas such as mantle cell lymphoma (MCL), diffuse large B-cell lymphoma (DLBCL), and Richter syndrome. You will develop and apply an innovative AI-driven 3D spheroid model to recreate the tumour microenvironment (TME) observed in vitro to enable more physiologically realistic drug testing and therapy prediction. As a PhD candidate, you are expected to bring your own creativity to the challenge: How can image-based AI analysis reveal drug effects? How can mechanistic computational models develo on spheroid morphology predict which treatment will work for which patient? You will be supported by experimental and clinical data using cutting-edge infrastructure including IncuCyte live-cell imaging, single-cell RNAseq, flow cytometry, and an access to the Dutch National Supercomputing facility Snellius to perform all the computational work. Your tasks and responsibilities include: Developing, optimising, and computationally characterising 3D primary spheroid models for MCL, DLBCL, and Richter syndrome using patient samples from in-house biobanks. Implementing and fine-tuning state-of-the-art AI image segmentation models for automated analysis of spheroid morphology and drug-induced structural changes. Building and refining the computational analysis pipeline: from image preprocessing and feature extraction (spheroid size, cell cluster count, area distribution, inter-cluster distance) to cellular Potts model (CPM) simulations of spheroid growth and drug responses. Integrating multi-modal data streams, such as live-cell imaging, bulk and single-cell RNAseq, and flow cytometry, into a unified predictive framework. Mechanistically connect in vitro drug response profiles with clinical patient characteristics to develop and validate a patient-specific treatment outcome prediction models. Presenting your findings at national and international conferences and publishing in peer-reviewed journals. Contributing to teaching and the broader scientific activities of both research groups. Co-supervising Bachelor, Master students and teaching assistance where appropriate. What we ask of you We are looking for someone who is genuinely excited about computational biology and not afraid of challenges. When the data or model outputs do not make any sense, you dig deeper! You communicate openly and thrive in a collaborative environment across different disciplines. Your experience and profile (below are examples, you don’t need to use them): MSc degree in computational science, (bio)medical sciences, biomedical engineering, or a related field with strong biological content. Interest in cancer biology and/or haematology. Affinity for quantitative data analysis. Programming experience in Python is an advantage but not a prerequisite. You are expected to develop these skills during the PhD. Excellent written and spoken English. Ability to work independently and as Annuncio in inglese. Fonte: Euraxess (Commissione europea).