The human brain has evolved over millions of years to process complex information from the world. Knowledge about how the human brain processes information has been greatly advanced by associating non-invasive neuroimaging data, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG), and well-controlled sensory inputs and well-controlled tasks. However, as a highly dynamic and nonlinear organ, how behaviors and brain activities are related to complex and naturalistic environment remains elusive.
We are looking for a post-doctoral fellow and a research assistant with machine learning expertise to work on this challenge. Specifically, the goal is to model the regional brain spatiotemporal activities based on complex and naturalistic stimuli, including, but not limited to, movie clips, musical pieces, and text articles. We aim at revealing such models that are i) stable across subjects and ii) with physiological interpretation. The goal is to identify such models and compare between groups of subjects from various ages or disorders conditions to sensitively detect how the brain adapts in development and aging.
The needed qualification includes:
• A Ph.D degree in engineering and science discipline with expertise in machine learning or deep learning (for a postdoc position).
• A M.S. degree in engineering and science discipline with expertise in machine learning or deep learning (for a research assistant position).
• Familiar with programming language, such as C++, C#, Matlab, or others
• Familiar with developmental tools of deep learning technologies, such as (but not limited to) Python, TensorFlow, Theano, etc.
• Experienced in signal acquisition, digital signal and/or image processing
• Background in biology and neuroscience are preferred by not required.
The responsibility of the recruited person includes
• Join a team of imaging engineers, doctors/surgeons, and cognitive neuroscientists for frequent discussion
• Establish the mapping between brain, behaviors, and the sensory inputs using the-state-of-the-art machine learning techniques with provided neuroimaging data and audiovisual materials
• Generate progress reports on a weekly basis.
• Publish results in academic conferences or journals.
We are a team of professors in neuroscience and engineering and doctors with secured funding. We provide the computational resources needed for data processing. The environment also provides the access to MRI, EEG, and MEG for data collection. We have extensive experience in structural MRI, functional MRI, MEG, EEG, and MEG data processing. When needed, we can coordinate the collection of data from both healthy controls and patients with different disorders.
The working location is at Academia Sinica. The salary will meet the standard of Academia Sinica.
Please send application including CV and a personal statement about your plan to address this challenge to Prof. Wen-Jui Kuo (firstname.lastname@example.org)
The application will be reviewed on a rolling basis. We will continue recruiting until positions are full.