RGCB Technical Support III Nanopore Recruitment 2026

RGCB Project Technical Support III Recruitment 2026: Overview

BRIC-Rajiv Gandhi Centre for Biotechnology (BRIC-RGCB) has announced a project-based Project Technical Support-III role with IndZita Biotech Pvt. Ltd., a startup incubated at the centre in Thiruvananthapuram, Kerala. The work belongs to an ICMR/MAHA MedTech-funded project developing a nanopore sensing system for non-invasive early detection of cervical cancer. Its focus is bringing artificial intelligence into an ultrasensitive sensing system, connecting sensor signals with machine-learning analysis.

Service Conditions and Caveats

• Project setting: You would join the work at IndZita Biotech, the startup named in this advertisement. The notice places this engagement at BRIC-RGCB in Thiruvananthapuram, so keep that project setting in mind when considering the role.
• Initial engagement: The appointment runs for one year initially, or until the project ends if that happens sooner. The shorter of these periods applies to the initial appointment.
• Extension: Continued engagement depends on your performance evaluation. An extension is conditional; the initial offer does not promise work beyond its stated term.
• Project-linked tenure: The position ends with the project. This condition applies even if you hope to continue working at the centre after the research work is complete.
• Future appointments: Taking this role gives you no claim to another RGCB position, whether permanent or otherwise. Treat it as an appointment tied to this funded project.
• Application format: Use the prescribed form for this advertisement. The centre says applications in another format will be rejected, so your choice of form matters.
• Separate applications: If you also want to pursue another advertised position, you need a separate form for that position. One application does not cover multiple roles.
• Government employees: If you already work in a government firm, apply through the proper channel as directed in the notice.

• Develop real-time preprocessing pipelines for raw sensor signals.
• Filter sensor inputs to reduce noise.
• Correct baseline drift in incoming signals.
• Build and train RNN, LSTM and Transformer models for signal classification.
• Validate models for real-time classification and feature extraction.
• Optimize machine-learning models for low-latency execution on the onboard edge device.