PhD Candidate: Crop Adaptation & Performance Analytics

IRTA

Lérida

On-site

EUR 20,000 - 26,000

Full time

3 days ago
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Job summary

IRTA in Lleida, Catalonia, Spain is seeking a PhD candidate to join the Sustainable Field Crops Program. The doctoral research, embedded in the Horizon Europe HEROCROP project, will study genetic and phenotypic determinants of crop performance across contrasting environments.

You will work with an international consortium on wheat and cross-crop collaborations, combining field phenotyping, genomic data and advanced statistics to develop predictive models.

Qualifications

  • Master’s degree in agronomy or related field.
  • Ability to organise experimental work and collect data.
  • Good written and spoken English.
  • Interest in statistics and computational methods (R or Python).
  • Experience with field trials or phenotyping is an advantage.

Responsibilities

  • Contribute to design, coordination and evaluation of experiments.
  • Characterise phenotypic diversity using standardized methods.
  • Apply field-based and high-throughput phenotyping to describe crop development.
  • Analyse genetic, environmental and management sources of variation (G×E×M).
  • Integrate phenotypic, genetic, environmental and agronomic datasets to develop predictive approaches.
  • Collaborate with HEROCROP partners and communicate results through papers/conferences.

Skills

Field trials
Phenotyping
R
Python
Data analysis
Teamwork
English communication
Genetics
Statistical analysis

Education

Master’s degree in agronomy or related field

Job description

IRTA in Lleida, Catalonia, Spain is seeking a PhD candidate to join the Sustainable Field Crops Program. The doctoral research, embedded in the Horizon Europe HEROCROP project, will study genetic and phenotypic determinants of crop performance across contrasting environments.

You will work with an international consortium on wheat and cross-crop collaborations, combining field phenotyping, genomic data and advanced statistics to develop predictive models.

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