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🚀 Project Research Scientist – III (Non-Medical) | IIT Delhi
Institution: Indian Institute of Technology Delhi (IIT Delhi) – Centre for Biomedical Engineering
📍 Location: Neuromechanics Research Lab (NRL), IIT Delhi, New Delhi, India 🇮🇳
🎓 Position: Project Research Scientist – III (Non-Medical)
👥 Openings: 2
💰 Compensation: ₹78,000 + HRA as applicable per month
🧠 Research Area: Brain-Computer Interface | EEG | Neuroscience Engineering | Machine Learning | Neurorehabilitation | Biomedical Engineering | Stroke Rehabilitation | Functional Electrical Stimulation
The Neuromechanics Research Lab (NRL) at the Centre for Biomedical Engineering, IIT Delhi, is inviting applications for two highly motivated researchers to join an interdisciplinary project focused on developing and clinically validating an EEG-based non-invasive Brain-Computer Interface (BCI) for upper-limb rehabilitation in chronic stroke survivors.
The project aims to develop an affordable, indigenously developed BCI system integrating 64-channel EEG with multichannel Functional Electrical Stimulation (FES) to help restore hand dexterity in individuals with chronic stroke.
🔬 What You’ll Work On
🧠 EEG Signal Processing
Preprocess EEG signals and curate extensive EEG datasets for training and validating machine-learning-based decoders.
🤖 Adaptive Machine Learning
Train, optimize, and implement an adaptive multi-class machine-learning decoder for online EEG classification.
👤 Patient-Specific Model Development
Develop algorithms that can fine-tune the BCI model to individual patients during calibration sessions.
💻 BCI Software & GUI Development
Collaborate across disciplines to develop a graphical user interface (GUI) for operating the BCI system.
⚡ Real-Time EEG–FES Integration
Integrate real-time EEG decoding with targeted multichannel FES outputs to elicit functional hand movements.
🏥 Clinical Trial Support
Provide technical troubleshooting and support during pilot randomized crossover clinical trials involving chronic stroke survivors.
🎯 Key Research Areas
The project will involve:
- Brain-Computer Interfaces (BCI)
- EEG signal processing
- Neuroscience engineering
- Biomedical engineering
- Machine learning
- Adaptive machine learning
- Multi-class classification
- Feature extraction
- Frequency band analysis
- Artifact rejection
- Neurorehabilitation
- Stroke rehabilitation
- Functional Electrical Stimulation (FES)
- Real-time signal processing
- Closed-loop systems
- GUI development
- Hardware-software integration
- Clinical validation
🧪 Research Activities
The successful researchers will contribute to several interconnected activities:
1. EEG Data Preprocessing
Process and curate large EEG datasets for decoder development and training.
2. Feature Extraction & Signal Analysis
Apply EEG signal-processing techniques including frequency-band decomposition, feature extraction, and artifact rejection.
3. Machine-Learning Decoder Development
Train and optimize adaptive machine-learning models for multi-class EEG classification.
4. Patient-Specific Calibration
Develop robust approaches for adapting the decoder to individual patients during calibration sessions.
5. Real-Time BCI Development
Implement real-time EEG decoding and integrate the system with hardware and software components.
6. EEG–FES Integration
Connect decoded neural signals with targeted multichannel Functional Electrical Stimulation to generate functional hand movements.
7. Clinical Trial Support
Assist with technical troubleshooting and system operation during pilot clinical trials.
🎓 Ideal Candidate
Applicants should have:
- A PhD in a relevant field.
- First Class throughout their academic qualifications.
- A strong publication record demonstrating research excellence.
- Strong background in EEG signal processing.
- Experience with feature extraction and frequency-band decomposition.
- Knowledge of EEG artifact rejection techniques.
- Expertise in adaptive machine-learning algorithms.
- Experience with multi-class classification.
- Proficiency in GUI development.
- Experience with real-time hardware-software integration.
- Ability to work with closed-loop systems.
- Strong interdisciplinary research and problem-solving skills.
Note: Qualifications may be relaxed for an exceptionally suitable candidate.
🌟 What You’ll Gain
- Research experience at IIT Delhi.
- Hands-on experience in Brain-Computer Interface technology.
- Experience working with 64-channel EEG systems.
- Exposure to adaptive machine learning and real-time neural decoding.
- Experience integrating EEG with Functional Electrical Stimulation (FES).
- Experience developing technology for stroke rehabilitation and neurorehabilitation.
- Exposure to clinical research and pilot randomized crossover trials.
- Experience working in an interdisciplinary neuroscience engineering and biomedical engineering environment.
- Opportunities to contribute to the development of an affordable, indigenous BCI system.
🧠 Research Focus
The project follows an integrated neuroengineering approach:
EEG Acquisition → Signal Processing → Feature Extraction → Machine Learning → Real-Time Classification → FES Integration → Functional Hand Movement → Clinical Validation
The primary objective is to develop and clinically validate a non-invasive EEG-based BCI system capable of interpreting neural activity and translating it into targeted stimulation for upper-limb rehabilitation in chronic stroke survivors.
The project combines neuroscience, EEG analysis, machine learning, biomedical engineering, real-time computing, FES, and clinical research.
👩🔬 Key Responsibilities
The successful researcher will be expected to:
- Preprocess EEG signals and curate large EEG datasets.
- Develop and optimize EEG signal-processing pipelines.
- Perform feature extraction and frequency-band decomposition.
- Implement artifact-rejection techniques.
- Train and optimize adaptive machine-learning models.
- Develop multi-class EEG classification algorithms.
- Implement online and real-time EEG decoding.
- Develop and contribute to BCI graphical user interfaces.
- Integrate hardware and software components for closed-loop operation.
- Develop patient-specific model fine-tuning approaches.
- Integrate EEG decoding with multichannel FES outputs.
- Support pilot randomized crossover clinical trials.
- Troubleshoot technical issues during experimental and clinical sessions.
- Collaborate with researchers from neuroscience, engineering, and clinical disciplines.
- Contribute to research publications and project outputs.
🏥 Research Environment
The researcher will join the Neuromechanics Research Lab (NRL) at the Centre for Biomedical Engineering, IIT Delhi.
The project brings together neuroscience engineering, data science, machine learning, biomedical engineering, BCI technology, and neurorehabilitation to develop an affordable technology for chronic stroke rehabilitation.
The two openings are intended for researchers with strong backgrounds in areas such as neuroscience engineering and data science.
📄 How to Apply
Interested candidates should apply by contacting:
📩 Prof. Deepak Joshi
Email: joshid@iitd.ac.in
Application Materials
Applicants should submit:
- Updated CV
- Brief Cover Letter
- Details of relevant technical experience in EEG processing or machine learning
Applicants are encouraged to highlight their experience in EEG signal analysis, machine learning, BCI development, real-time systems, or related areas.
📌 Job Details
Institution: Indian Institute of Technology Delhi (IIT Delhi)
Centre: Centre for Biomedical Engineering
Laboratory: Neuromechanics Research Lab (NRL)
Position: Project Research Scientist – III (Non-Medical)
Openings: 2
Location: IIT Delhi, New Delhi, India
Project: Development and Clinical Validation of an Electroencephalography-based Non-Invasive Brain-Computer Interface for Upper Limb Rehabilitation in Chronic Stroke Survivors
Research Focus: Brain-Computer Interface | EEG | Machine Learning | Neuroscience Engineering | Neurorehabilitation | Stroke Rehabilitation
Core Technology: 64-Channel EEG + Multichannel Functional Electrical Stimulation (FES)
Compensation: ₹78,000 + HRA as applicable per month
Minimum Qualification: PhD in a relevant area with First Class throughout
Preferred Expertise: EEG Signal Processing | Machine Learning | Multi-Class Classification | Feature Extraction | Artifact Rejection | GUI Development | Real-Time Systems
Technical Skills: EEG Processing | Adaptive ML | Signal Analysis | Real-Time Hardware-Software Integration | Closed-Loop Systems
Clinical Focus: Chronic Stroke | Upper-Limb Rehabilitation | Hand Dexterity
Application Mode: Email
Application Contact: Prof. Deepak Joshi
Application Email: joshid@iitd.ac.in
Application Documents: CV | Brief Cover Letter
Qualification Relaxation: May be considered for exceptionally suitable candidates
Category
Project Research Scientist | Research Scientist |Stroke Rehabilitation | Functional Electrical Stimulation | Data Science | BCI Research | Research Jobs India
🏷️ Tags
#IITDelhi #ProjectResearchScientist #ResearchScientist #BiomedicalEngineering #BrainComputerInterface #BCI #EEG #Neuroscience #NeuroscienceEngineering #MachineLearning
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