The University of Luxembourg aspires to be one of Europe’s most highly regarded universities with a distinctly international and interdisciplinary character. It fosters the cross-fertilisation of research and teaching, is relevant to its country, is known worldwide for its research and teaching in targeted areas, and is establishing itself as an innovative model for contemporary European Higher Education. The University`s core asset is its well-connected world-class academic staff which will attract the most motivated, talented and creative students and young researchers who will learn to enjoy taking up challenges and develop into visionary thinkers able to shape society.
Within the University, the Luxembourg Centre for Systems Biomedicine (LCSB) is a highly interdisciplinary research centre (IC), integrating experimental biology and computational biology approaches in order to develop the foundation of a future predictive, preventive and personalized medicine.
The Systems Control Group (SCG) seeks a highly skilled Postdoctoral Research Associate. The project aims to enhance the outcome of Deep Brain Stimulation (DBS) in Parkinson’s disease (PD) through the individualization and optimization of stimulation settings. The goal is to replace the current practice of selecting stimulation pulse width, amplitude, and frequency by trial and error with a model-based patient-specific calculation (more details below). The project is funded by the EU Joint Programme on Neurodegenerative Disease Research (JPND) and is part of a consortium consisting of neurologists, electrical and control engineers, computer scientists, movement scientists, and neurosurgeons from four European countries.
Postdocs are expected to interact with the rest of the group, especially with PhD students and support grant writing.
Hold a Ph.D. degree in (applied or theoretical) mathematics, theoretical physics, engineering or computer science, with a strong mathematical background. Ideal candidates would have a good understanding of dynamical systems, control theory, optimisation, or machine learning. Excellent working knowledge of English is required.
Applications should be submitted online and include:
Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by email will not be considered.
The University of Luxembourg embraces inclusion and diversity as key values. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff.
Further details of the project:
Using a range of mathematical tools, this project aims to improve our understanding of how DBS affects the brain. It combines all available datasets in the project to create models capturing patient-specific and personalized brain dynamics before and after DBS stimulation. Models will capture stimulation at a wide range of frequencies, pulse widths and amplitudes across different contact configurations. Data includes EEG, peripheral sensors (such as accelerometers) and LFPs. EEG data will also lead to a network analysis that reveals which connections between different brain regions are gained/lost. Changing stimulation parameters will give us a nonlinear view of changes in brain connections. This will build new hypotheses on the effect of DBS stimulation on different areas of the brain, and help us better predict effective stimulations.
For further information, please contact Jorge Goncalves (firstname.lastname@example.org).Leer más
|Título||Postdoc (Research Associate) in Dynamics and Machine Learning for Medical Applications|
|Employer||University of Luxembourg|
|Job location||6, rue Richard Coudenhove-Kalergi, L-1359 Luxembourg|
|Publicado||febrero 11, 2021|
|Fecha límite de solicitud||No especificado|
|Tipos de trabajo||Postdoctorado  |
|Campos||Biomedicina,   Ciencias Clínicas,   Biología de Sistemas,   Informática en las Matemáticas, Ciencias Naturales, Ingeniería y Medicina,   Física Matemática,   Física Médica,   Física Teórica,   Aprendizaje de Máquina  |