Skip to main content

PhD Position in Transformative Technologies and Smart Watersheds Project University of Waterloo



PhD Position in Transformative Technologies and Smart Watersheds Project University of Waterloo

Title of Opportunity: Application of novel airborne Ku and L-band SAR observations for watershed-scale seasonal snow mapping

Start date: 1 September 2020 or negotiable

A fully funded four-year PhD position is available in the 'Transformative Sensor Technologies and Smart Watersheds for Canadian Water Futures' project (TTSW) at the University of Waterloo. The position is part of Global Water Futures: Solutions to Water Threats in an Era of Global Change, a large collaborative initiative involving multiple Canadian universities and partner organizations. TTSW aims to develop, test, and employ advanced terrestrial, sub-orbital, and satellite remote sensing tools targeted to support research regarding the emerging spectrum of water related issues throughout cold regions.

More Information

The CryoSAR airborne radar system is a new and unique CFI-funded synthetic aperture radar (SAR) system specifically designed to make fully polarimetric and InSAR-capable observations of cold season environments at Ku and L-band frequencies. The successful PhD candidate will explore ways that CryoSAR observations of snow can be used to estimate the distributions of snow water equivalent (SWE) at watershed-scales. To achieve this, the candidate will be expected to develop remote sensing modelling approaches that focus on SWE retrievals from SAR backscatter and InSAR observations. The successful candidate will be encouraged to be an active participant in winter field campaigns in prairie and alpine environments to characterize SWE and snowpack microstructure properties. They will also have access to a dedicated high-performance GPU-based processing system capable of conducting end-to-end SAR processing and SWE retrieval modelling.

The successful candidate will work under the supervision of Dr. Richard Kelly, and will collaborate with researchers at partner organizations involved with the CryoSAR project.

Eligibility

Ideally, you will have a strong background in quantitative remote sensing science, preferably with an understanding of Earth system science processes, especially hydrological science. Ideally, you should hold a degree in geographical science, geophysics, Earth science or engineering. The candidate should have strong analytical capabilities with a high degree of comfort across coding environments such as C, Python, R, IDL, Matlab or other programming languages commonly used in remote sensing. Strong communication skills are essential and the candidate should be able to work both independently and within a group setting both in field environments and in the lab.

Full funding is available for four years, pending satisfactory progress through the PhD program.

Application Instructions

Interested applicants should submit a cover letter stating their motivation and experience. In addition, a curriculum vitae, unofficial transcripts, and contact information for three references should be included in a single .pdf file and sent to Dr. Richard Kelly (rejkelly@uwaterloo.ca) with [PhD-TTSW-RichardKelly-2020] in the subject line.

We thank all applicants for their interest. However, only selected candidates will be contacted.



....

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...