Anders Eklund

Linköping University is recruiting a PhD student in biomedical engineering for a project on deep learning for medical images , with a focus on early detection of lung cancer . The position is within the Department of Biomedical Engineering (IMT) and is part of the DDLS (Data-Driven Life Science) initiative. The research topic combines medical image analysis , computer vision , deep learning , machine learning , statistics , and biomedical engineering . The project aims to develop methods for detecting lung nodules from chest CT volumes and clinical variables, and to classify nodules as benign or malignant. The work uses the Swedish SCAPIS dataset and involves large-scale medical data, AI for healthcare, and precision medicine/diagnostics. The successful candidate will work in a research group focused on analysis of medical images, collaborating with medical doctors at Linköping University Hospital through CMIV, as well as with researchers in computer vision and statistics/machine learning at Linköping University. The environment includes access to strong AI infrastructure such as Berzelius and local computing resources. Eligibility highlights: a Master’s degree or equivalent in biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related field; at least 60 advanced credits in a relevant area; documented English proficiency; strong skills in computer vision and/or medical image analysis, deep learning, mathematics, and Python programming. Funding and employment: the position is a paid PhD employment at Linköping University. The starting salary is SEK 36,400, with salary progression according to local agreement. The appointment is normally four years full-time, with possible extension up to five years depending on teaching and departmental duties. Application window: applications must be submitted no later than 2026-05-15 (CET). Apply via the university vacancy page.

Linköping University is recruiting a PhD student in Biomedical Engineering for a project on deep learning for medical images , with a focus on early detection of lung cancer . The position is part of the DDLS (Data-Driven Life Science) initiative and the Wallenberg National Program for Data-Driven Life Science , a major Swedish research program supporting data-driven life science, AI, and computational methods. The research topic sits at the intersection of medical image analysis , computer vision , deep learning , biomedical engineering , and precision medicine and diagnostics . The project uses chest CT volumes and clinical variables from the Swedish SCAPIS cohort to develop methods for detecting lung nodules and distinguishing benign from malignant nodules. The work also involves combining imaging with clinical data such as age, sex, and smoking status, and aims to improve early cancer detection using large-scale medical datasets. The successful candidate will work at the Department of Biomedical Engineering (IMT) at Linköping University, in a research group focused on medical image analysis, with collaborations across the university and with clinicians at Linköping University Hospital through CMIV . The environment also connects to the computer vision laboratory and the division of statistics and machine learning, offering a strong interdisciplinary setting for AI in healthcare. Eligibility highlights: a Master’s degree in biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related field; at least 60 advanced-level credits in a relevant area; documented English proficiency; and strong skills in computer vision/medical image analysis, deep learning, mathematics, and Python programming. The role also expects independence, precision, efficiency, and good communication skills. Funding and employment: this is a paid doctoral employment, not a scholarship. The starting salary is SEK 36,400, with local salary progression and a normal duration of four years full-time, extendable up to five years depending on teaching and departmental duties. Application window: the deadline is 2026-05-15 (CET). Applicants should use the university’s online application button on the vacancy page and submit all required documents before the deadline.