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Phd position (m/f/d) in computer vision for plant disease detection

München
Technical University of Munich
Inserat online seit: 17 Dezember
Beschreibung

PhD Position (m/f/d) in Computer Vision for Plant Disease Detection

17.12., Wissenschaftliches Personal

We are seeking a highly motivated PhD student to join an international research collaboration between the Chair of Digital Agriculture (TUM) and the University of São Paulo, Brazil. This position focuses on developing advanced computer vision methods and hardware setup for detecting and predicting plant diseases in soybean cultivation.

About Us

The Chair of Digital Agriculture is engaged in simulation models of plant stands to improve plant system understanding and its control and optimization. Our research links Artificial Intelligence methods to topics in agriculture and spans many scales from field to global.

Project Description

Soybean is a critical crop for global food security and agricultural economies. This research project aims to develop AI-driven computer vision systems that can accurately identify plant diseases and predict plant health in soybean crops, enabling early intervention and improved crop management. You will work on developing the imaging system as well as novel machine learning approaches for image analysis and disease classification using field data from German and Brazilian agricultural trials.

Responsibilities

1. Design the imaging system for generating image datasets from agricultural machines
2. Develop and implement computer vision algorithms for plant disease detection and health estimation in soybeans
3. Design and train deep learning models using agricultural image datasets
4. Collaborate with researchers at the University of São Paulo and agricultural domain experts
5. Publish research findings in peer-reviewed journals and present at international conferences

Required Qualifications

6. Master’s degree in computer science, or a closely related field
7. Strong programming skills in Python and experience with deep learning frameworks (PyTorch, TensorFlow, or similar)
8. Solid foundation in computer vision and image processing techniques
9. Excellent written and oral communication skills in English
10. Ability to work independently and as part of an international team

Desired Qualifications

11. Familiarity with cloud computing platforms and large-scale data processing
12. German language skills
13. Publications in computer vision or machine learning venues
14. Prior experience with agricultural applications or remote sensing

We Offer

15. A fully funded PhD position for 3 years (TV-L E13)
16. Opportunity to work on real-world agricultural challenges with significant impact
17. International research collaboration with leading institutions
18. Research-related trips to Brazil and international conferences
19. Supportive research environment with experienced supervisors
20. The possibility of research stays abroad

Application Materials

21. Please submit the following documents:
22. Curriculum vitae, including a list of publications (if applicable)
23. Short cover letter describing your motivation and relevant experience
24. Electronic copies of academic diplomas and relevant employment references

Application Deadline

15 January

Earliest Starting Date

1 March

The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

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