NET

1st Class M.Sc, Masters degree in related discipline life sciences, Genetics, Biotechnology with 2 years research experience.

M.Pharm with minimum four years experience in medicinal & aromatic plants OR PhD in Botany, Life science, Pharmacy. 1st class M.E, M.Tech, M.Sc. in relevant branch of Engineering and Technology, Materials Science, Chemistry and Polymer Science

Title of Project Engineering of Long-lasting Breast Hydrogel Implants for Cancer Immunochemotherapy. Essential Qualifications MSc Biotechnology, Chemistry, Pharmacy, Pharmacology qualified with any national level test NET, CSIR-UGC, GATE, GPAT.

M.Sc Organic Chemistry, Analytical Chemistry, Biochemistry, Phytochemistry Or M.Pharmacy, M. S. Pharmaceutical Chemistry, Medicinal Chemistry, Natural Products, Pharmacognosy.

The candidate should have M.Sc. degree in Botany, Microbiology, Biochemistry, Biotechnology, Life Sciences from a recognized university with minimum 55% marks and NET, GATE qualified. candidates with experience in the area of Fungal Diversity QR related field will he preferred.

M.Sc. in life sciences with NET, DBT-JRF, GATE or equivalent exam cleared. Practical experience in protein expression and purification, ribosome isolation cryo- EM data processing, atomic model building and model validation.

Effect of cisplatin-encapsulated HPV virion- based targeted delivery vehicle on cellular and cervicectomy samples using high-resolution Cryo-Electron Tomography. Cryo-Electron Microscope Imaging facility for biological specimens, Cells and Tissue. First class MSc in Biological Sciences with valid JRF, NET. Work experience in Electron Tomography, Electron Microscopy, Protein purification and characterization, Tissue and cell culture, confocal microscopy will be preferred.

Masters, Integrated Masters in Natural or Agricultural or Pharmaceutical Sciences MVSc, Animal Sciences OR Bachelors degree in Engineering or Technology or Medicine from a recognized University or equivalent

To undertake proposed research involving data analysis, computational modeling and programming for the clinical data on diabetes. Experience in data analysis, computer programming and mathematical modeling is desirable.