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

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...

Seismicity and Earthquakes, Isostasy and Gravity

1. Seismicity and Earthquakes in the Indian Subcontinent Key Concept: Seismicity Definition : The occurrence, frequency, and magnitude of earthquakes in a region. In India, seismicity is high due to active tectonic processes . Plate Tectonics 🌏 Indian Plate : Moves northward at about 5 cm/year. Collision with Eurasian Plate : Causes intense crustal deformation , mountain building (Himalayas), and earthquakes. This is an example of a continental-continental collision zone . Seismic Zones of India Classified into Zone II, III, IV, V (Bureau of Indian Standards, BIS). Zone V = highest hazard (e.g., Himalayas, Northeast India). Zone II = lowest hazard (e.g., parts of peninsular India). Earthquake Hazards ⚠️ Himalayas: prone to large shallow-focus earthquakes due to active thrust faulting. Northeast India: complex subduction and strike-slip faults . Examples: 1897 Shillong Earthquake (Magnitude ~8.1) 1950 Assam–Tib...

geostationary and sun-synchronous

Orbital characteristics of Remote sensing satellite geostationary and sun-synchronous  Orbits in Remote Sensing Orbit = the path a satellite follows around the Earth. The orbit determines what part of Earth the satellite can see , how often it revisits , and what applications it is good for . Remote sensing satellites mainly use two standard orbits : Geostationary Orbit (GEO) Sun-Synchronous Orbit (SSO)  Geostationary Satellites (GEO) Characteristics Altitude : ~35,786 km above the equator. Period : 24 hours → same as Earth's rotation. Orbit type : Circular, directly above the equator . Appears "stationary" over one fixed point on Earth. Concepts & Terminologies Geosynchronous = orbit period matches Earth's rotation (24h). Geostationary = special type of geosynchronous orbit directly above equator → looks fixed. Continuous coverage : Can monitor the same area all the time. Applications Weather...

Network data model

GIS, a network data model is used to represent and study things that are connected like a web — for example, roads, rivers, railway tracks, water pipes, or electric lines . It focuses on how things are connected and helps us solve problems like finding the best route, the nearest hospital, or where water will flow. Nodes → Points where things meet or end (e.g., road intersections, railway stations, pumping stations). Edges → Lines connecting the nodes (e.g., roads, pipelines, cables). Topology → The "rules" of connection — which node is linked to which edge. Attributes → Extra details about each part (e.g., road speed limit, pipe size, traffic volume). How It Works 🔍 Make the Network Model Start with a map of lines (roads, pipes, rivers) and mark how they connect. Run Analyses Routing → Find the shortest or fastest path. Closest Facility → Find the nearest hospital, petrol station, etc. Service Area → Find how far y...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...