Skip to main content

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

Popular posts from this blog

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...

Logical Data Model in GIS

In GIS, a logical data model defines how data is structured and interrelated—independent of how it is physically stored or implemented. It serves as a blueprint for designing databases, focusing on the organization of entities, their attributes, and relationships, without tying them to a specific database technology. Key Features Abstraction : The logical model operates at an abstract level, emphasizing the conceptual structure of data rather than the technical details of storage or implementation. Entity-Attribute Relationships : It identifies key entities (objects or concepts) and their attributes (properties), as well as the logical relationships between them. Business Rules : Business logic is embedded in the model to enforce rules, constraints, and conditions that ensure data consistency and accuracy. Technology Independence : The logical model is platform-agnostic—it is not tied to any specific database system or storage format. Visual Representat...

Approaches of Surface Water Management: Watershed-Based Approaches

Surface water management refers to the strategies used to regulate and optimize the availability, distribution, and quality of surface water resources such as rivers, lakes, and reservoirs. One of the most effective strategies is the watershed-based approach , which considers the entire watershed or drainage basin as a unit for water resource management, ensuring sustainability and minimizing conflicts between upstream and downstream users. 1. Watershed-Based Approaches Watershed A watershed (or drainage basin) is a geographical area where all precipitation and surface runoff flow into a common outlet such as a river, lake, or ocean. Example : The Ganga River Basin is a watershed that drains into the Bay of Bengal. Hydrological Cycle and Watershed Management Watershed-based approaches work by managing the hydrological cycle , which involves precipitation, infiltration, runoff, evapotranspiration, and groundwater recharge. Precipitation : Rainfall or snowfall within a...

Raster Data Structure

Raster Data Raster data is like a digital photo made up of small squares called cells or pixels . Each cell shows something about that spot — like how high it is (elevation), how hot it is (temperature), or what kind of land it is (forest, water, etc.). Think of it like a graph paper where each box is colored to show what's there. Key Points What's in the cell? Each cell stores information — for example, "water" or "forest." Where is the cell? The cell's location comes from its place in the grid (like row 3, column 5). We don't need to store its exact coordinates. How Do We Decide a Cell's Value? Sometimes, one cell covers more than one thing (like part forest and part water). To choose one value , we can: Center Point: Use whatever feature is in the middle. Most Area: Use the feature that takes up the most space in the cell. Most Important: Use the most important feature (like a road or well), even if it...

Disaster Management international framework

The international landscape for disaster management relies on frameworks that emphasize reducing risk, improving preparedness, and fostering resilience to protect lives, economies, and ecosystems from the impacts of natural and human-made hazards. Here's a more detailed examination of key international frameworks, with a focus on terminologies, facts, and concepts, as well as the role of the United Nations Office for Disaster Risk Reduction (UNDRR): 1. Sendai Framework for Disaster Risk Reduction 2015-2030 Adopted at the Third UN World Conference on Disaster Risk Reduction in Sendai, Japan, and endorsed by the UN General Assembly in 2015, the Sendai Framework represents a paradigm shift from disaster response to proactive disaster risk management. It applies across natural, technological, and biological hazards. Core Priorities: Understanding Disaster Risk: This includes awareness of disaster risk factors and strengthening risk assessments based on geographic, social, and econo...