PhD position PhD in seasonal prediction of harmful algaeblooms Nansen Environmental and Remote Sensing Center

The Nansen Center is an independent non-profit research foundation located in Bergen, Norway. We conduct multidisciplinary research with a focus on the marine environment, cryosphere and atmosphere, where scientific activities are closely integrated with innovation and service development. The Arctic is one of our main areas of attention.

NERSC takes an active part in training and capacity building for students and young scientists, as well as dissemination to stakeholders in public and private sector and society in general.

The Nansen Center is an international workplace with some 70 employees from 24 nations.

The Doctoral fellowship position

The Nansen Center has a vacancy for a Doctoral fellowship (PhD candidate) in the field of climate prediction. The position is an institute-defined PhD topic, which is fully funded by the Research Council of Norway for a three-year period. The candidate will be employed at NERSC and formally complete the doctoral degree at the University of Bergen.

NERSC introduced the Ensemble Kalman Filter (EnKF) data assimilation method in the 1990s and has maintained its further theoretical development and application, including combination of data assimilation with machine learning. The center develop and maintain two state-of-the-art prediction systems: The Earth System seasonal-to-decadal predictions with the Norwegian Climate Prediction Model (NorCPM) and the real-time ocean and sea ice forecasting system for high latitudes within the European Copernicus Marine Environmental Monitoring Services.

‍

The candidate will focus on research for the identification of harmful environmental conditions related to ocean fisheries and aquaculture. The candidate will analyse in-situ, satellite observations and model simulations and explore the use of machine learning techniques to predict the risks of occurrence of harmful algae bloom in Norway at sub-seasonal to seasonal time scales. The prediction scheme will be fed by existing dynamical climate predictions (e.g NorCPM, C3S) and real-time ocean colour satellite data.

‍

The candidate will be supervised by Dr. François Counillon who has expertise on data assimilation and climate prediction and Dr. Julien Brajard who has expertise on machine learning and remote sensing.

Qualifications

For ranking of qualified candidates, the following criteria will be evaluated:

A master´s degree or equivalent (eligible for registration as a PhD candidate at University of Bergen) in applied mathematics, Earth system science, physics, engineering, or computer science is required

Experience with machine learning and/or data assimilation is a strong asset

Knowledge in oceanography, biogeochemistry or climate dynamics would be beneficial

Good skills in programming and data analysis software is expected

Good written and oral communication skills in English

Personal Qualities

We are seeking a highly motivated candidate with excellent problem resolving skills and who will actively participate in and cooperate with our scientists. The candidate will gain experience from a research institute as well as the formal university education.

We offer

Interesting and challenging tasks

Supervision by acknowledged professionals within data assimilation, machine learning, remote sensing and data analysis

Work in a research-intensive, international, informal, and social academic work environment

Salary and social benefits according to national regulations for doctoral fellowships

Access to supercomputing facilities

Information

For further information about the position, please contact

Dr. François Counillon for scientific questions: e-mail: Francois.counillon@nersc.no, tlf: 99351953

Head of Administration Christine Sivertsen for administrative issues: e-mail: Christine.sivertsen@nersc.no, tlf: 90788115.

‍

Submission deadline: August 1st, 2020

‍

Areas of Research

Atmospheric Physics & Meteorology

Fisheries & Aquaculture

Marine Biology

....

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc
🌏🌎
🌐🌍

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...

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...

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

1. 1832 - Early Spatial Analysis in Epidemiology: - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants. - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis: - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis. - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping: - Photozincography development allows maps to be split into layers for vegetation, water, etc. - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography: - Waldo Tobler's 1959 publication details using computers for cartography. - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

Vineesh V, Geography

Search This Blog