Skip to main content

PhD programme in Regional Science & Economic Geography.

The Gran Sasso Science Institute, a public research-intensive university dedicated to doctoral education in L'Aquila, Italy, has advertised 10 fully funded doctoral positions in its 4-year English-taught PhD programme in Regional Science & Economic Geography. The programme offers training in both quantitative and qualitative research methods in economic geography and regional studies.

L'Aquila is a mid-size historical town in central Italy, surrounded by mountains in a largely unspoiled natural environment. It is the capital city of the Abruzzo region. Rome can be reached from L'Aquila in about one hour and half by bus.

Here is the programme's official statement: 

The Ph.D. program in "Regional Science and Economic Geography" is a cutting-edge and interdisciplinary program designed to explore the intricate relationship between space and socioeconomic phenomena, by employing mixed methods and interdisciplinary approaches. This Ph.D. program equips students with the skills to offer evidence-based policy recommendations, rooted in robust empirical findings and established causal relationships, and fosters research in economic and/or human geography. It aims to address the challenges posed by globalization, urbanization, climate change, and development by fostering a deeper understanding of these processes. We welcome students with different backgrounds, including (but not limited to) applied economics, economic and/or human geography, or sociology, who share an interest in these issues.

The PhD Programme lasts four years. The Academic Year will start on November 1st, 2024. The GSSI awards scholarships until the thesis dissertation and for a maximum of four years. The yearly gross amount of the scholarship is € 16.243,00. An additional 50% on a monthly basis can be awarded for research periods abroad if approved by the GSSI. During their first year, PhD students will be offered free accommodation by the GSSI. In the remaining three years they will receive from the university a contribution to their housing costs.

* Students with interests in critical and heterodox regional studies and economic geographies are encouraged to apply *

Applications will close on May 23rd, 2024.





Comments

Popular posts from this blog

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...

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...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

Applications of Remote Sensing

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition.  Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...

Classification of Mixed Pixels

In remote sensing, a mixed pixel (also called a mixed cell or mixel ) is a pixel that contains more than one land-cover type inside its area. This is very common when: spatial resolution is coarse (e.g., 30 m, 250 m) land cover is patchy or complex boundaries between features exist (forest–agriculture, land–water edges) Traditional hard classification assigns each pixel to one single class only → either forest or soil or water. But mixed pixels contain fractions of several classes simultaneously, so hard classification produces errors. Two major solutions are: Spectral Mixture Analysis (SMA) Fuzzy Classification 1. Spectral Mixture Analysis (SMA) (Also called Linear Spectral Unmixing ) ✔ Concept SMA assumes that the reflectance recorded by a pixel is a linear combination of the reflectance of pure materials within that pixel. These pure materials are called endmembers . ✔ Endmembers Endmembers are spectrally pure classes found...