Post-doc Position | Neural and Intelligence Engineering Center, Institute of Science and Technology

Post‑doc Position | Neural and Intelligence Engineering Center, Institute of Science and Technology for Brain‑inspired Intelligence, Fudan University, Shanghai, China

Location:

Fudan University, Handan Road, Wujiaochang, Yangpu District, Shanghai, China

Qualification:

PhD

Salary

Offer:

¥250k ~400k /year (≈ $35k–58k / year)

Discipline:
Science & Engineering, Medical & Health Science

Research Projects Research Direction I:
Intelligent Algorithms and Systems of Brain‑Computer Interfaces for Neurostimulation and Neuromodulation

• Establish neural circuit and neural network models for diseases such as Parkinson’s disease, dystonia, epilepsy, depression, etc. Develop intelligent neuromodulation methods and technologies for functional and circuit specificity, core technologies including brain‑computer interface devices and chips, and integration system platforms. Advance studies in personalized, adaptive, function‑specific and precise neuromodulation mechanisms and technology transfer for clinical applications.

Project i.i. Brain‑computer Interfaces for Neuromodulation Based on Reinforcement Learning and Other Machine Learning Approaches

• Establish a reinforcement learning model for adaptive neuromodulation picturing stimulus‑response relations, based on dynamic neural responses to electrical nerve stimulation (e.g. EEG, LFP).
• Realize real‑time recognition of the state of brain function and intelligent neuromodulation.
• Carry out application research on closed‑loop and non‑invasive vagus nerve stimulation, closed‑loop deep brain stimulation, etc.

Project i.ii. System Integration Technologies for Brain‑computer Interfaces for Neurostimulation and Neuromodulation

• Integrate brain‑computer interface devices and chips to remotely monitor multiple physiological parameters of brain function.
• Develop multi‑sensor and multi‑channel stimulation system platforms, which integrate machine learning algorithms for real‑time processing and analysis of large‑scale neural data, to realize neuromodulatory brain‑computer interfacing in the sense‑computation‑stimulation closed‑loop.

Research Direction II:
Digital Therapies Based on Cognitive‑Behavioral Intervention

• Develop a quantitative and intelligent cognitive‑behavioral therapy theory targeting to neuromodulation and intervention for neuropsychiatric disorders (e.g. insomnia, depression). Develop key technologies, including dynamic quantitative modeling of cognitive behavior, human‑computer interaction in emotional behavior, non‑invasive brain‑computer interface, wearable monitoring, etc. Develop digital cognitive behavior training intervention systems to reach personalization and precision in brain intervention strategies.

Project ii.i. Digital Therapy for Sleep Modulation

• Develop intervention and neuromodulation technologies for cognitive behavior training. Apply wearable multi‑hop sensors and personalized smart bands for monitoring real‑time sleep pattern, daily behavior, cognitive change and biological indices. Build a sleep pattern recognition model and design personalized, real‑time and digital intervention plans based on multidimensional data collected from a large sample size, leading to online, quantitative and long‑term stable intelligent sleep management and intervention.

Project

ii.ii. Intervention in Child Anxiety and Depression

• Investigate the impact of parenting styles and family relationships on children’s cognitive development, using neuroscience and cognitive neuroscience methods. Combine developmental psychology and cognitive neuroscience theories with mobile technology to develop an application for emotion management and parent‑child communication with intelligent analysis technology, grounded on cognitive behavior training. Taking individual problem as a start point, this application is designed to be a closed‑loop management tool capable of learning, case study, method practice, tracking and feedback.

Project

ii.iii. Quantitative and Intelligent Management of Parkinson’s Disease

• Monitor movements and biological status of Parkinson’s disease patients with wearable sensors. Build a pharmacodynamic model showing the relation between patient’s status and drug effect. Build…