PhD Positions - Remote Sensing for Precision Agriculture and Plant Phenotyping TU München

-





PhD Positions - Remote Sensing for Precision Agriculture and Plant Phenotyping TU München


The Precision Agriculture Lab at Technical University of Munich (TUM) is seeking applications for Research Assistant positions (TV-L E13, 50%) for pursuing Ph.D. degree with a research focus on remote sensing for precision agriculture and plant phenotyping. The position is limited to 36 months. Extension is negotiable depending on funds. The Precision Agriculture Lab is newly established within the Department of Life Science Engineering, TUM School of Life Sciences. We conduct interdisciplinary research from a diversity perspective of precision agriculture (or precision/smart farming). We focus on studying plant-environment interactions and their control from multiple scales by applying and integrating a range of imaging, remote sensing, statistical modeling, and computational techniques. We are seeking creative candidates who are enthusiastic about interdisciplinary research in precision agriculture – For instance, using cutting-edge sensing and modeling techniques to quantitatively characterize crop stress response and field variability, plant traits, and biodiversity; studying the underlying eco-physiological and genetic basis; and formulating technical strategies for smart farming and sustainable agriculture. Candidates will have the opportunity to work within a stimulating research environment with an interdisciplinary team. The successful candidates will be employed by TUM. You will not only work on your doctoral dissertation but also perform a wide range of research and teaching tasks. You will produce project reports, present research findings in conferences, and publish research findings in peer-reviewed journals.
Requirements:
• Master's degree in remote sensing, agricultural science, ecology, geoinformation science, agricultural engineering, biosystems engineering, or related fields.
• Expertise in remote sensing, handling big data (e.g. spectral and spatial data analyses).
• Skills in programming (e.g., R/Python/Matlab) and image processing.
• Knowledge about precision agriculture, GIS, drones, plant phenotyping, biodiversity.
• Desirable to have experience in computer vision, machine learning and deep learning.
• Proficiency in English (both oral and writing skills).
• Motivation to perform field and lab work.
• Ability to work independently as well as collaboratively in an international and interdisciplinary team.

As an equal opportunity and affirmative action employer, TUM encourages application from women as well as from all others who would bring additional diversity to the university's research and teaching strategies. Preference will be given to disabled candidates with essentially the same qualifications.

Application:
To apply, please submit your application including the following documents: 1) letter of motivation, 2) CV, 3) copies of university degree certificates and transcripts, 4) names and contact information of three references. Please send you application in a single PDF file, with the subject format 'TUM Precision Agriculture PhD Position Application', to pa@wzw.tum.de by 15.09.2020 for full consideration. Interviews of invited candidates will be held at the end of September 2020.

Contact:
Prof. Dr. Kang Yu
Precision Agriculture
Technical University of Munich
Dürnast 3, D-85354 Freising, Germany
Phone: +49 (0)81 6171 5001
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.



....
Warm Regards

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
http://geogisgeo.blogspot.com
🌏🌎
🌐🌍

Comments

Post a Comment

Popular posts from this blog

Spatial feature manipulation in remote sensing

-
Image
Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching
Read more

Grey level thresholding. Level slicing. Contrast stretching lo p

-
Image
Grey level thresholding. Level slicing. Contrast stretching. Image enhancement Lillesand and Kiefer (1994) explained the goal of image enhancement procedures is to improve the visual interpretability of any image by increasing the apparent distinction between the features in the scene. This objective is to create "new" image from the original image in order to increase the amount of information that can be visually interpreted from the data. Enhancement operations are normally applied to image data after the appropriate restoration procedures have been performed. Noise removal, in particular, is an important precursor to most enhancements. In this study, typical image enhancement techniques are as follows: Grey level thresholding Grey level thresholding is a simple lookup table, which partitions the gray levels in an image into one or two categories - those below a user-selected threshold and those above. Thresholding is one of many methods for creating a binary mask for an i
Read more

Spatial filtering, Remote Sensing. Low-pass filter. High-pass filter

-
Spatial filtering encompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is related to the concept of image texture. It refers to the frequency of the variations in tone that appear in an image. "Rough" textured areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while "smooth" areas with little variation in tone over several pixels, have low spatial frequencies. A common filtering procedure involves moving a 'window' of a few pixels in dimension (e.g. 3x3, 5x5, etc.) over each pixel in the image, applying a mathematical calculation using the pixel values under that window, and replacing the central pixel with the new value. The window is moved along in both the row and column dimensions one pixel at a time and the ca
Read more