Engineer for AI-based image analysis and optical sensing of plant diseases

At the University of Göttingen -Public Law Foundation-, DNPW - Abt. Pflanzenkrankheiten und Pflanzenschutz, there is a position as Engineer for AI-based image analysis and optical sensing of plant diseases

**Entgeltgruppe 11 TV-L/80%**to be filled. Starting date is as soon as possible. The position is initially a two-year fixed-term contract, with a possibility to convert it to a permanent contract after a successful evaluation.

The position supports research on plant diseases through the development and application of AI-based image analysis workflows and optical sensing approaches. The successful candidate will work at the interface of scientific programming, machine learning, image analysis, plant disease experiments and optical sensing technologies.

Your profile

We are seeking a highly motivated and technically skilled engineer with strong organizational skills and the ability to work independently and collaboratively in an interdisciplinary research environment. Applicants must hold at least a Bachelor’s degree in computer science, data science or a related field, combined with a strong background in scientific programming, AI-based image analysis, machine learning and data management.

Candidates should have practical experience in processing large image datasets, implementing image analysis pipelines and training, testing and validating machine learning or deep learning models. Experience with RGB, multispectral, hyperspectral, thermal infrared or 3D/LiDAR-based image data will be considered an asset. The ability to document workflows, code and data structures clearly, and to update and expand technical knowledge and skills as required, is essential. A good command of English is required. Knowledge of German is a plus, but not required.

Your tasks

• Development, implementation, testing and documentation of software workflows for image annotation, data import, preprocessing, quality control, model training, model testing, model validation and structured storage of results from plant disease experiments.

• Application and adaptation of image analysis, machine learning and deep learning methods for RGB, multispectral, hyperspectral, thermal infrared and 3D/LiDAR-based image data.
• Technical support for optical sensing experiments under controlled conditions, including sensor setup, calibration, measurement workflows and data quality control.
• Support for field-based optical sensing campaigns using ground-based robotic platforms and UAV-based systems, including georeferencing, orthomosaic generation, radiometric calibration, image registration and integration with experimental field data and metadata.
• Training and technical support of MSc students, doctoral researchers, postdoctoral researchers and other members of the division in the use of image analysis workflows, annotation tools, data management structures and model validation procedures.

What we offer

• An interdisciplinary and collaborative, team-oriented working environment at the interface of plant pathology, optical sensing, artificial intelligence and data science.

• Integration into a scientifically active and growing division that develops modern quantitative approaches for studying plant diseases. Close collaboration with researchers, technical staff, doctoral researchers and students, with opportunities to contribute technical expertise to a wide range of research projects.
• A family-friendly, diverse and international working environment in which we value diversity and equality.

The University of Göttingen is an equal opportunities employer and places particular emphasis on fostering career opportunities for women. Qualified women are therefore strongly encouraged to apply in fields in which they are underrepresented. The university has committed itself to being a family-friendly institution and supports their employees in balancing work and family life. T