진학프로석·박사 채용 접수 플랫폼

Czech Technical University in Prague (CTU) offers a fellowship program, the CTU Global Postdoc Fellowship. This new and attractive two-year fellowship-program offers excellent researchers who have recently completed their PhD the chance to continue their research career at CTU. Fellows receive a two year fellowship and become members of a team led by a mentor.

The scholarship applicant must meet the following conditions on the date of application:

• • be no more than 7 years since obtaining the first Ph.D. degree,
• • Ph.D. studies at a university outside the Czech Republic or have completed at least a one-year working research stay abroad (outside the Czech Republic),
• • be an author (co-author) of three or more publications in a journal with IF or CORE A*/A conference paper.

We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s disease. The research will center on identifying robust, interpretable predictors of motor improvement after subthalamic nucleus (STN) DBS, integrating preoperative clinical, motor, neuropsychological, and imaging data. Building on recent critiques of overfitted prediction models, the project will implement a structured, multi-tiered analytical framework, comparing classical regression techniques with modern penalized and hierarchical modeling strategies. The successful candidate will contribute to outcome definition, data analysis, and model development, and will have the opportunity to engage in advanced statistical methods such as dimensionality reduction and Bayesian modeling. This project offers access to a rich, curated clinical dataset and collaboration with leading neurologists, neurosurgeons, and data scientists

Skills/Qualifications

The ideal candidate holds a PhD in neuroscience, biomedical engineering, computational biology, biostatistics, or a related discipline, with a strong background in clinical data analysis and predictive modeling. Experience with multivariate regression techniques, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is considered a strong advantage. Proficiency in statistical programming languages such as Python, R, is expected, as is the ability to work with complex clinical datasets and handle missing data thoughtfully.