Normative modeling is one promising approach for this that aims to characterize variation across a healthy cohort before making predictions so that subjects that deviate from the resulting normative model can be detected as outliers in an anomaly detection setting and the pattern underlying the deviation can be further analyzed to understand the biological underpinnings. Normative modeling is naturally applied to large clinical cohorts; therefore it demands high computational resources for processing big data in reasonable time. The Fastr package provides a workflow engine for developing reliable pipelines in medical image data analysis. We, at the predictive clinical neuroscience lab, are opening the call for an internship position with the main aim to develop an end-to-end highly-parallelized normative modeling pipeline for structural and functional MRI data in the Fastr framework. This position provides a unique opportunity for the trainee in order to learn the state-of-the-art high-performance computing techniques in neuroimaging data analysis. Our traineeship is designed to prepare the student to apply the acquired knowledge to a wide range of problems in precision psychiatry with the prospect of publication in prestigious venues. Our work is highly interdisciplinary, and applicants must have a strong background in programming (Python, bash). Previous experience with neuroimaging data analysis packages such as FSL is a plus. Candidates with backgrounds in computer science, engineering, and related areas are welcome to apply. The internship is based in the Donders Institute that brings together more than 600 researchers from 35 countries with the goal of understanding the mechanistic underpinnings of human cognition and behavior in health and disease. The trainee will have access to the state-of-the-art computational facilities, e.g., high-performance computing cluster and several GPUs, to accomplish the traineeship goals. Interested applicants should apply by sending their CV to firstname.lastname@example.org .
- Marquand, Andre F., Iead Rezek, Jan Buitelaar, and Christian F. Beckmann. “Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies.” Biological psychiatry 80, no. 7 (2016): 552-561.
- Achterberg, Hakim C., Marcel Koek, and Wiro J. Niessen. “Fastr: A workflow engine for advanced data flows in medical image analysis.” Frontiers in ICT 3 (2016): 15.