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variant analysis, ideally in plants -genotype-environment association (GEA/EAA) or GWAS -programming in Python, with extensive experience working in Linux and multiple HPC environments. -population genomics
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, multi-omics analyses and systematic bioinformatics techniques is a strong advantage. Excellent programming skills (Python/R) and a solid training in AI or machine learning are highly preferred. A strong
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required to meet the eligibility criteria. Of secondary importance are: Experience with Python and relevant libraries for machine learning, optimization and simulation. Documented expertise in simulation
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. Required qualifications: PhD in a field such as physics, systems biology, applied mathematics, machine learning, or related fields. Strong programming skills (e.g. Python) and experience with modern ML
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, Bioinformatics, Agricultural Engineering, or a related field. Knowledge of plant physiology. Strong programming skills in Python and/or R. Excellent scientific writing and communication skills in English. Desired