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

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...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

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...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to 1832 , when Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in 1854 , when John Snow inv...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Vineesh V, Geography

Search This Blog