www.acad.jobs : academic jobs worldwide – and the best jobs in industry
                
     
Position: Research Fellow (Machine Learning & Brain Imaging)
Institution: National University of Singapore
Location: Singapore
Duties: The candidate will have the opportunity to interact with faculty, researchers, and students from both medicine and engineering departments and contribute to the development of machine learning approaches to solve problems in neuroscience and medicine. Strong interest in studying brain networks in health and disease using multimodal neuroimaging methods and computational/machine learning approaches would be a plus
Requirements: Candidates must have a passionate enthusiasm for research, a strong background in at least one of the following fields: machine learning, neuroimaging analyses, computational neuroscience, neuroinformatics, mathematics/statistics, or related fields. Knowledge in cognitive neuroscience, psychology, or neuropsychiatric disorders is a plus but not necessary. He/she should possess the ability to take the initiative and work independently in a highly collaborative and international research environment. He/she needs to demonstrate creativity, critical thinking, technical independence, and excellent communications/multitasking skills. Proven skills in neuroimaging data analyses and machine learning is a plus
   
Text: By continuing to use and navigate this website, you are agreeing to the use of cookies. Accept Close Skip to main content Language English (United Kingdom) View profile Language English (United Kingdom) View profile Home Page View All Jobs for NUS Language English (United Kingdom) View profile Search by Keyword Show More Options Search by Location (enter Kent Ridge, Outram, Bukit Timah or others) Loading... Department Job Category Organisation × Send me alerts every days Create Alert × Send me alerts every days Share this Job Research Fellow (Machine Learning & Brain Imaging) Apply now » Apply now × Apply for Job × × × Enter your email to apply Date: 05-Sep-2021 Location: Kent Ridge Campus, SG Company: National University of Singapore Background Postdoctoral Fellowship in Machine Learning & Brain Imaging at National University of Singapore The National University of Singapore invites applications for a research fellow position (post-doctoral fellowship) in the Multimodal Neuroimaging in Neuropsychiatric Disorders Laboratory (MNNDL), Center for Sleep and Cognition and Center for Translational Magnetic Resonance, Yong Loo Lin School of Medicine, and Department of Electrical and Computer Engineering. More information on the laboratory is available at www.neuroimaginglab.org. Background The MNNDL group at NUS is a multidisciplinary team studying the human neural bases of cognitive functions and the associated vulnerability patterns in aging and neuropsychiatric disorders using multimodal neuroimaging and machine learning methods. We are interested in the large-scale brain structural and functional networks in healthy developing and aging brain and symptoms-related changes in diseases such as neurodegenerative disorders and psychosis. Computational and machine learning methods are developed to analyze multimodal neuroimaging (MR/fMRI/DWI/EEG/PET), genetic, and behavioral data. By integrating longitudinal behavior, neuroimaging, and genotype data, our long-term goal is to investigate the interactions among brain network dynamics, behavior, diseases, and genotypes to develop non-invasive biomarkers for early detection, differential diagnosis, progression monitoring, and treatment design. Job Description The candidate will have the opportunity to interact with faculty, researchers, and students from both medicine and engineering departments and contribute to the development of machine learning approaches to solve problems in neuroscience and medicine. Strong interest in studying brain networks in health and disease using multimodal neuroimaging methods and computational/machine learning approaches would be a plus. Qualifications Candidates must have a passionate enthusiasm for research, a strong background in at least one of the following fields: machine learning, neuroimaging analyses, computational neuroscience, neuroinformatics, mathematics/statistics, or related fields. Knowledge in cognitive neuroscience, psychology, or neuropsychiatric disorders is a plus but not necessary. He/she should possess the ability to take the initiative and work independently in a highly collaborative and international research environment. He/she needs to demonstrate creativity, critical thinking, technical independence, and excellent communications/multitasking skills. Proven skills in neuroimaging data analyses and machine learning is a plus. Key Attractions The lab has access to large-scale local and international neuroimaging datasets in health and disease. Key attractions are access to a high-performance computing cluster (GPU/CPU and more than 300TB of data), two 3T Prisma MR scanners, and an MR compatible digital EEG system as well as collaboration opportunities with an excellent network of domestic and international scientists and clinicians. Appointments will be made on a two-year contract basis in the first instance, with the possibility of extension. A competitive package will be provided based on experience. Interested applicants are welcome to email Associate Professor Juan Helen Zhou at helen.zhou@nus.edu.sg (Twitter: @HelenJuanZhou) with the application letter, curriculum vitae, and contacts of at least two references. Only shortlisted candidates will be invited for an interview. More Information Location: Kent Ridge Campus Organization: Yong Loo Lin School of Medicine Department : Medicine Employee Referral Eligible: No Apply now » Apply now × Apply for Job × × × Enter your email to apply Find similar jobs: View All Jobs for NUS, View All Jobs, Academic and Research Positions Legal Branding guidelines Contact us 中文 © National University of Singapore. All Rights Reserved.
Please click here, if the job didn't load correctly.







Please wait. You are being redirected to the job in 3 seconds.