Master degree in Technology, Statistics, Biometrics, Mathematics with good understanding of Plant Biology and Genetics or related disciplines
Image processing, 3D point cloud analysis, algorithm implementation Meshlab
Programming skills (R, C++) and big data management, high-performance computing
Proficiency in written and spoken English
Ability to interact effectively in a multi-cultural and multidisciplinary environment
Creativity and ability to work independently and in a team-based environment
Ability to draft high-quality research articles
Background in quantitative genetics (e.g. QTL, GWAS mapping), crops stress physiology and molecular biology is an advantage
Insight into agricultural production systems
Assessment of phenotypic variations in components of canopy size (i.e. tillering, leaf appearance, and leaf longitudinal and lateral expansion) and canopy structure in sorghum using high throughput-phenotyping platform “LeasyScan”
Development of algorithms to disentangle the components of canopy size and structure from the 3D-point clouds generated by LeasyScan, implementing automated 3D-point-clouds data
Explore the functional associations between canopy size components at the level of physiological relations and genetic linkages.
Cross-disciplinary communication and collaboration, team player
Training of students (national and international) and technical staff
Field and platform experiments designing, data analysis and interpretation; preparation of reports and journal articles
System Analysis for Climate Smart Agriculture (SACSA) at ICRISAT:
The Laboratory of Crop Physiology and Modelling is part of the SACSA team and undertakes agri-systems research focusing on understanding the production environments and development of strategies to enhance crop abiotic stress adaptations in the semi-arid farming systems of South Asia and sub-Saharan Africa (e.g. drought and climate change cropping systems adaptation). The program focuses on ICRISAT’s mandate crops (sorghum, pearl millet, small millets, groundnut, chickpea, pigeonpea). The labs activities involve basic research to dissect the environments, to better understand the physiological mechanisms of drought adaptation and their genetics, and the development of high throughput screening methods to support breeding programs. The team employs the crop modelling approaches to better frame genetic and agronomic management targets; i.e. identify interventions to maximize productivity under the range of environment x management circumstances. The team is also expanding into the area of grain and stover quality recognizing that product quality and safety (e.g. fodder value of stover, grain quality, contamination) are ultimately important traits to consider in breeding targets for improvement of crop market value and human health within the integrated crop-livestock systems.
This is a Consolidated (CON) position for two years renewable based on the performance of the incumbent and the institute’s continuing need for the position.
How to Apply:
Applicants should apply on or before 28 May 2019 with latest Curriculum Vitae, and the names and contact information of three references that are knowledgeable about your professional qualifications and work experience. All applications will be acknowledged; however only short listed candidates will be contacted.
Please Contact Employer Directly.
For More Details Visit: https://www.icrisat.org/
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