Bioinformatic
Pubblicato il 03-09-2026 - Altro in Bardi
Postdoctoral Researcher in Multimodal AI for Cancer Research
Work location: Via Rita Levi Montalcini 4, ***** | Pieve Emanuele (MI)
Contract type: Coordinated and Continuous Collaboration Agreement (Co. Co. Co.)
Why Humanitas
Choosing Humanitas means joining an environment where quality of care is built every day through clinical expertise, innovation and teamwork, where everyone contributes to the patient care journey.
Who we are
Humanitas University is an international institution dedicated to the Life Sciences, closely integrated with the IRCCS Humanitas Research Hospital and the Humanitas hospital network. The University combines education, research, and clinical practice within a highly international environment. Medicine and Surgery and MEDTEC School are taught entirely in English, and the University currently hosts more than 3,000 students, with international students accounting for 43% of the Medicine cohort. Teaching activities take place on a modern, sustainable 35,000 sqm Campus designed to foster interaction among students, researchers, and faculty, with facilities including the Anatomy Lab and the Simulation Center – the only center in Italy fully accredited by the European Society for Simulation in Medicine for excellence in healthcare simulation training.
The context
The Computational Biology Laboratory led by Prof. Charlotte Ng is seeking a highly motivated data scientist or AI researcher to develop computational methods for precision oncology. Our research combines clinical data with bulk, single-cell and spatial omics,
digital pathology and other biomedical data to study tumour heterogeneity and improve patient stratification and treatment response prediction.
The successful candidate will develop and evaluate machine learning approaches for modelling complex, heterogeneous and incompletely observed biomedical data. The work may encompass multimodal representation learning, deep generative modelling, transfer learning and robust predictive modelling. The candidate will work closely with computational biologists, AI researchers, experimental scientists and clinicians, while having scope to develop new methodological directions within the laboratory's research programme.
Key tasks and responsibilities
Develop, optimize and rigorously evaluate machine learning models for high-dimensional biomedical and multi-omics data.
Investigate generative and representation learning approaches for multimodal data integration and modelling.
Design appropriate benchmarking and validation strategies, including assessment of model robustness, generalizability and biological relevance.
Apply the resulting methods to clinically relevant questions in cancer biology, biomarker discovery and treatment response prediction.
Collaborate with computational,
experimental and clinical researchers and contribute to scientific publications and presentations.
The person we are looking for:
Essential qualifications
PhD in computer science, machine learning, statistics, mathematics, computational biology or a related discipline.
Strong foundations in machine learning, statistical modelling or artificial intelligence.
Experience developing models using Python and a modern deep-learning framework, preferably PyTorch.
Ability to design rigorous computational experiments and critically evaluate model performance.
Excellent communication, organizational and scientific writing skills in English.
Ability to work independently while collaborating effectively within a multidisciplinary team.
Desirable experience
Deep generative models, representation learning, transfer learning or multimodal learning.
Analysis of high-dimensional, low-sample size datasets.
Biomedical and omics data analysis.
Model interpretability, uncertainty estimation or learning with incomplete modalities.
Image-based or other multimodal biomedical data.
What we offer
Working at Humanitas means contributing to a future where care, wellbeing and professional excellence grow together. For this reason, we offer:
a wellbeing programme;
practical work-life balance measures
training and development opportunities to foster talent and enhance skills in a dynamic and innovative environment.
This offer is open to candidates in compliance with Legislative Decrees ********, ******** and ********.