Postdoc in Machine Learning Methods for Cancer Risk Assessment

Job description We are looking for a highly motivated postdoctoral researcher for a joint project between the Taheri Lab and the Lundberg Lab, funded by the Digital Futures (DF). The position is based at KTH Royal Institute of Technology and SciLifeLab (Science for Life Laboratory). KTH is Sweden’s largest technical university and a top-ranked European institution in engineering and technology. This postdoctoral position will be hosted at the Department of Computational Science and Technology (CST) and constitutes an equal joint appointment between Taheri’s Lab at the School of Electrical Engineering and Computer Science (EECS) and Lundberg’s Lab at the School of Chemistry, Biotechnology and Health (CBH). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include: Development of ML/DL methods for multi-omics data analysis. Design and implementation of computational tools and software for cancer risk prediction. This position offers the chance to engage in cutting‑edge interdisciplinary research at the intersection of ML and cancer research, and to contribute to the development of novel tools for cancer risk assessment with real potential impact on healthcare. Qualifications Requirements A doctoral degree or an equivalent foreign degree in computer science, statistics, bioinformatics, computational biology, applied mathematics, or a closely related field. This eligibility requirement must be met no later than the time the employment decision is made. Strong background in ML and/or statistical modelling, demonstrated through thesis work and/or scientific publications. Solid programming skills, for example in Python (and/or R), and experience with relevant ML libraries (e.g. PyTorch, TensorFlow or similar). Experience with software/tool development for research, including good practices in reproducible code (e.g. Git, notebooks, pipelines). Demonstrated experience in analyzing large-scale, high-dimensional datasets including multi-modal data integration and hands-on experience with high-performance computing (HPC) environments Ability to work independently and in an interdisciplinary team, and to drive research projects forward. Excellent skills in written and spoken English. English is the working language in the research environment and is required for scientific writing, presentations, and collaboration with international partners. Preferred qualifications A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline. Documented experience with bioinformatics and/or multi‑omics cancer data integration (e.g. genomics, transcriptomics, proteomics, imaging). Experience from interdisciplinary collaboration with biologists, clinicians, or industry partners. Experience in supervising or co‑supervising students, teaching, or mentoring. A strong publication record in relevant journals or conferences in ML, bioinformatics, or related fields. Important personal skills involve the ability to collaborate, communicate clearly in an interdisciplinary environment, and work in a structured and proactive way. Awareness of diversity and equal opportunity issues, with specific focus on gender equality Great emphasis will be placed on personal skills.