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Projects

Euclid Space Telescope – dark energy and dark matter, FHNW School of Computer Science

School of Computer Science


A software infrastructure that enables efficient and robust pipeline processing of the vast amount of data in distributed processing sites.

image-1152-2d748ce931b2d1afad80d1b64eb6c854.jpeg

Testimonial

Objective

Management and distribution of large volumes of space data across a global data network.

Background

Euclid is a space telescope designed to observe the expansion of the Universe. Its mission is to improve our understanding of dark energy and dark matter. The data are collected from the darkest regions of the sky, free from light contamination caused by our galaxy or our solar system.

Euclid will observe approximately ten billion sources of light. The entire survey will consist of hundreds of thousands of images and dozens of petabytes of data. The key challenge is to distribute these vast volumes of data efficiently across a network of ten data centres around the world.


Project details

Type
Research project
Research areas
Astroinformatics and Space Sciences, Data Engineering and High-Performance Computing (HPC)
Topics
Informatik und Data Science, Data Science und Engineering and Artificial Intelligence und Machine Learning
University
Hochschule für Informatik FHNW, FHNW School of Computer Science / Institute of Data Science
Funding
Europäische Raumfahrtagentur ESA, Prodex
Management
Prof. Dr. Martin Melchior

Contact us

For further information about the FHNW School of Computer Science or to discuss potential collaboration opportunities, please contact us.

Martin Melchior

Prof. Dr. Martin Melchior

Lecturer for Data Science
Phone
+41 56 202 77 07 (Direct)
E-Mail
martin.melchior@fhnw.ch

More Projects


DrugSafety: Semi-automated reporting of side effects of drugs

A system for extracting relevant information from medical reports in order to report side effects of drugs semi-automatically to the responsible authorities.
Institute
School of Engineering and Environment

Marvel: Real-time pollen information

Together with our project partners, we develop zero-shot learning and other machine learning tools for recognising pollen particles anywhere in the world. As a result, it will be easier to create reliable pollen weather forecasts.
Research field
AI, Machine Learning & Natural Language Processing (NLP), Exploratory Data Science and Image Processing & Computer Vision

Knowledge Assistant – AI-based information retrieval tool

AI can organise and retrieve information – at least in theory. In practice, turning corporate data into a user-friendly resource is a big challenge. Our collaboration project tackles the challenge.
Research field
AI, Machine Learning & Natural Language Processing (NLP)

School of
Computer Science FHNW University of Applied Sciences and Arts Northwestern Switzerland

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