Phd Position in Machine Learning for Neuroimaging
vor 6 Monaten
**Contexte**:
The Lausanne University Hospital (CHUV) is one of five Swiss university hospitals. Through its collaboration with the Faculty of Biology and Medicine of the University of Lausanne and the EPFL, CHUV plays a leading role in the areas of medical care, medical research and training.
The Radiology Department has a strong research focus, advancing MR acquisition, image processing, and machine learning for radiology and clinical research. The department is part of the Center for Biomedical Imaging (CIBM), a joint undertaking of the EPFL, University of Geneva (UNIGE), UNIL, and the Geneva University Hospital (HUG).
HES-SO, the University of Applied Sciences and Arts Western Switzerland, excels in applied research, partnering with university hospitals and UNIGE's and UNIL's medical and psychology departments. Its industry ties, global engagement, and research centers address societal challenges, positioning it as a prominent player in Swiss higher education and interdisciplinary advancement.
The GEMMI lab (Geneva Musical Minds Lab) was founded in 2019 and is part of the Geneva University Neurocenter. It focuses on experience-driven brain and behavioral plasticity following musical, artistic, and physical activity over the lifespan aiming to boost child development and countervail age-related cognitive and cerebral decline.
Founded in 2020 at CHUV and UNIL's Radiology Department, the Translational Machine Learning Lab develops machine learning for diverse real-world imaging and medical data to enhance disease comprehension and patient outcomes.
**RATIONALE OF THE PROJECT**
Executive functions (EF) regulate goal-oriented behavior, encompassing initiation, shifting, inhibition, working memory, extending to complex tasks like abstract reasoning and planning.
Crucial for cognitive development, EF anchor memory, attention, problem-solving, decision-making, and language, consistently used daily, and pivotal for academic achievement.
The Art and Development project, funded by the Swiss National Science Foundation, aims at a breakthrough in understanding the development of EF, academic achievement, and its cerebral underpinnings in young school children.
A large-scale, long-term randomized controlled trial (RCT) in cognitive neuroscience is urgently needed, contrasting musical training with other arts and a control group. Through machine learning, this study will form a data-driven model of brain and EF growth (ages 6-8), appraising arts influence by comparing music and visual arts. Outcomes might reshape education, also considering children from disadvantaged backgrounds.
**Mission**:
The goal of this doctoral position, in collaboration with a postdoctoral researcher within the same team and our other project partners, will be to develop new machine learning algorithms to model combined brain and behavioral development supporting executive function and academic achievements over the first two years of academic education (6 to 8-year-old children).
In this RCT, 150 children will receive 2-year intensive string instrumental training in an orchestra setting, or visual arts, or participate in an active control group (n=50 in each group).
At baseline, after one year and after two years, an extensive EF battery and four different types of MRI will be measured : T1-weighted MRI: MP2RAGE (gray matter assessment), Multishell Diffusion Weighted Imaging (DWI; white matter assessment); fMRI (auditory and visual working memory), and Resting State fMRI.
The "Art and Development" project, a collaboration among CHUV/UNIL, FPSE, CIBM UNIGE, and HES-SO Geneva School of Health Sciences (HEdS-GE), is overseen by Prof. Dr. Clara James (principal investigator and PhD co-director) and Dr. Jonas Richiardi (PhD Director). Abundant computing resources, GPUs, and active engagement with the Consortium and scientific partners from other institutions, along with two postdocs and MSc students, are integral. The role involves MSc student supervision and working primarily at CHUV/UNIL Lausanne, with weekly gatherings at the HEdS-GE.
**Profil**:
- MSc in computer science, applied mathematics, biomedical engineering, statistics, data science, statistical physics, statistics, electrical engineering, or related field
- Good training in linear algebra, calculus, statistics
- Good knowledge of Python 3, including relevant machine learning libraries such as scikit-learn, Pytorch 1.5+, and Tensorflow 2.
- English proficiency is mandatory, and good French is an asset
- Demonstrated previous experience in machine learning and deep learning
- Genuine interest in the interdisciplinary field of the study
- Experience in medical imaging, graph representations, image processing are an advantage.
You will be part of a welcoming team of scientists, Ph.D. students, and multiple partners, which requires a strong team spirit and professionalism. Excellent communication and interpersonal skills are as important as technical skills. This project a
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