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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You
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functionalities (GUI and web-service) Participate in field work organization, sampling plan establishment and in-situ data acquisition Your Profile PhD in environmental sciences or computer science, with a proven
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reports) support of e.g., interns, students, doctoral candidates in the processing, evaluation and interpretation of data and in the conception and implementation of research projects Requirements
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). to design and test scenarios based on observational constraints for tropical coastal wetland systems, in particular mangrove forests, with a case of Indonesia. to collaborate with members of ZMT to design
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master's degree (or equivalent diploma) and a PhD in meteorology, oceanography, or a related natural or geoscientific discipline with significant physical and mathematical components. It is essential
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Qualifications / Experience: • A PhD in Physics, Geoscience or a related field • Proven expertise in numerical modelling using super computing clusters • Excellent knowledge of atmospheric physics • Proficiency in
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completed scientific university education (Master's/Diploma) with PhD in horticulture sciences, agricultural sciences, biology environmental protection or a related field in-depth knowledge of organic
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phenotyping, including image analysis evaluations, for trait quantification Handle NGS datasets for RNAseq or SNP detection and linkage analysis using R Your qualifications and skills: You have a PhD or
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under small temperature gradients (ΔT ≤ 30 K). Design and Optimize novel fabrication techniques to achieve high packing densities in thermoelectric generators (TEGs) for powering IoT sensor networks
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science