PhD Student (f/m/d) in AI/ML for Personalized Technical Medicine and Parkinson’s Disease Detection Interdisciplinary Transformation University (IT:U) in United Kingdom

Interdisciplinary Transformation University (IT:U) in Linz, Austria is offering one fully described PhD position in its Doctoral School / PhD Program in Interdisciplinary Computing. The project sits at the intersection of artificial intelligence, machine learning, brain health, and personalized technical medicine, with a clear application to the early detection and subtyping of Parkinson’s disease.

The PhD project, Integration of Novel biomarkers for Detection, Evaluation of Early Signatures, and Developmental insights of Parkinson's Disease (INDEED-PD), is carried out in cooperation with IT:U, Johannes Kepler University Linz (JKU), the Medical Faculty of JKU, and Kepler University Hospital (KUK). The research focus is on integrating multimodal data such as EEG, ECG, and MRI/fMRI with cutting-edge AI methods to build robust analytical approaches for early diagnosis and disease characterization.

The successful candidate will work within a multidisciplinary team and collaborate closely with another PhD student focusing on large-scale electronic health records. Supervision is provided by Dr. Jie Mei (IT:U) and Dr. Erich Kobler (Institute for Machine Learning, Johannes Kepler University Linz), with additional collaboration opportunities involving Dr. Spiros Denaxas, Dr. Daniel Klotz, and Dr. Kai Loewenbrück. The position is a four-year fixed-term PhD appointment with a workload of 40 hours per week, based on-site in Linz.

The research tasks include developing AI/ML models for computer vision, signal processing, and time-series analysis; designing rigorous experiments and baseline comparisons; managing research activities with collaborators; and publishing results in international conferences and journals. The role is well suited to a candidate with strong quantitative training, practical experience in machine learning, and a genuine interest in computational approaches to neurological disease.

Eligibility highlights include a completed or nearly completed master’s degree in a quantitative field such as computer science, physics, electrical engineering, statistics, or mechatronics; 1–2 years of AI/ML experience; strong Python skills; and fluent English. Experience with large-scale medical datasets is considered an advantage. The post offers structured supervision, interdisciplinary research training, and access to a dynamic, international environment with modern research infrastructure.

Funding is provided as a gross salary aligned with the FWF salary rate of EUR 3,776.13 for a 40-hour week. The application deadline is 2026-08-05. Applicants should apply online and upload a CV, degree certificates, transcripts, a motivational letter, and up to three recommendation contacts.