Skip to main content

IIT Delhi-University of Queensland International Joint PhD with Scholarships and fellowship



IIT Delhi-University of Queensland International Joint PhD in Science, Engineering, Management, Humanities: Apply by March 22
Start your future on Coursera today.
     
BY: USHA | 24 Feb 2020 11:17 AM

 
The University of Queensland, Australia and IIT Delhi have created a joint research programme titled UQ-IITD Academy of Research (UQIDAR).

UQIDAR will attract the best global talent, including elite students, academics, researchers and scientists to work on goal-directed, cross-disciplinary grand challenges that are of interest to Australia, India and the global community and that also align with The University of Queensland (UQ) and Indian Institute of Technology (IITD) research strengths. UQIDAR will enable UQ and IITD to enrol the brightest and most talented students in a joint PhD with joint supervision from both institutions. It is anticipated that the majority of students (i-students) will be recruited into the joint-PhD program in Delhi, and there will be a small cohort of Australia-anchored scholars (q-students).

i-students will spend 3 years in India and a minimum of one year in Australia while
q-students will spend 3 years in Australia and one year in India.
It is expected that candidature will be a maximum of 4 years in all disciplines, depending on a students progress, with scholarships offered for a maximum of 4 years. Both i-students and q-students will be expected to undertake some coursework. Upon successful completion of the program, students will be offered a PhD degree from both UQ and IITD.

Students of the Academy will
Gain a joint global qualification from two institutions (UQ and IITD) in 4 years;
Receive a generous scholarship;
Be in a position to take advantage of world-class facilities and resources and gain exposure to a new research ecosystem, network and environment; and
Benefit from global expertise via dual supervision between UQ and IITD as well as possible industry input.
The collaboration will involve strong industry linkages whereby industry will be involved in supporting PhD students. Industry supported PhD scholars will work on challenging research problems posed and defined by industry partners of the UQIDAR. Industry supervisors will co-guide the students along with UQ and IITD supervisors. The collaboration will also enable the establishment of a mobility or fellowship scheme to enable academics and postdoctoral fellows to spend time at each institute, expanding research linkages and offering career development opportunities for early career researchers.

Comments

Popular posts from this blog

Radar Remote Sensing SAR

1. Radar and Radar Remote Sensing RADAR stands for Radio Detection and Ranging . It is an active remote sensing system that transmits microwave energy toward the Earth's surface and records the energy that is returned to the sensor as an echo or backscatter . Unlike passive optical remote sensing, radar does not depend on sunlight. Therefore, it can operate day and night and, at suitable wavelengths, can acquire data through clouds, haze and light rain. Hence, radar is widely described as an all-weather, day-and-night remote sensing technology . Basic principle Microwave pulse → transmission → interaction with surface → backscatter/echo → antenna receives signal → signal processing → radar image 2. Microwave Energy Radar systems use microwave electromagnetic radiation , generally in wavelength ranges from approximately 1 mm to 1 m . Important radar bands include: Band Approx. wavelength Common applications ...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Lidar

LiDAR (Light Detection and Ranging) is an active remote sensing technology that measures distances by illuminating a target with laser pulses and analyzing the time it takes for the reflected light to return. Unlike passive systems (e.g., cameras, multispectral sensors), LiDAR provides its own energy source (laser), allowing it to operate both day and night and even penetrate through vegetation canopies . 🔹 How LiDAR Works (Step-by-Step Process) Laser Pulse Emission The system emits rapid, short pulses of laser light (commonly in the near-infrared wavelength, 1064 nm ). Some systems emit up to hundreds of thousands of pulses per second . Interaction with Target Surface The laser beam strikes objects such as vegetation, buildings, or bare ground. Depending on the object's structure, part of the pulse may scatter or reflect. Return Signal Detection The sensor records multiple returns : First Return → typically vegetation canopy tops. ...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types  1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...