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Patrick Schratz
Data Scientist at cynkra GmbH
Zurich, Switzerland

Intro

I am a PhD student within the GIScience group at the Department of Geography. I focus on the application of statistical- and machine learning techniques to address research questions related to environmental issues. Beside the scientific part I am also interested in the development of (geo)statistical programming solutions to simplify todays data science challenges. Check the projects section for detailed information. I like to write about the Linux world, R stuff and other technical topics that I find interesting.

Tech Stack

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r
r(6 years)
git
git(5 years)
latex
latex(5 years)
CSS
CSS(4 years)
docker
docker(2 years)
exoscale
exoscale(1 years)
nginx
nginx(1 years)
ansible
ansible(1 years)

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Work Experiences

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cynkra GmbH
Jun 2020 - Present (11 months)
Zürich, Schweiz Current workspace
Currently Patrick Schratz supports the cynkra GmbH

Patrick Schratz's scores will be added to this company.

Data Scientist
r git
Friedrich-Schiller-Universität Jena
4 years 6 months
Jena, Germany
Researcher
May 2019 - Jun 2020 (1 year 1 month)
Researcher
Oct 2016 - Apr 2019 (2 years 6 months)
Project: EU LIFE Healthy Forest
Research Assistant
Apr 2015 - Mar 2016 (11 months)
Project: Forest-DRAGON 3
LMU - Ludwig-Maximilians-Universität München
May 2019 - Jun 2020 (1 year 1 month)
Remote
Research Engineer
Chinese Academy of Forestry
Aug 2015 - Oct 2015 (2 months)
Beijing City, China
Research Intern
Project work within Forest-DRAGON 3
Centre for Geographical Analysis
Mar 2014 - May 2014 (2 months)
Stellenbosch, South Africa
Intern
TU Ilmenau
Feb 2014 - Apr 2015 (1 year 2 months)
Remote
Research Assistant
GIS support in the project “Explaining the nexus between
entrepreneurial culture and economic factors in cities,
regions, and countries” promoted by the “Fritz-Thyssen-
Stiftung”

Portfolio

Add some compelling projects here to demonstrate your experience
EU LIFE Environment and Resource Efficiency project „Healthy Forest”
Apr 2016 - Present
The EU LIFE Environment and Resource Efficiency project „Healthy Forest” aims to design and apply advanced methodologies to achieve a more sustainable forest management in the field of control and prevention of forest decline caused by invasive and pathogenic agents. An important step toward this objective is the development of an early detection system and monitoring of forest health in the field, in the laboratory and by remote sensing. The sub-project conducted by the Geographic Information Science (GIScience) group at the Department of Geography of the University of Jena focuses on spatial modeling of forest disease potential at the regional scale, and on the application of computational and statistical techniques to identify significant predictors of forest decline in high-dimensional hyperspectral remote-sensing data.
ESA Forest-DRAGON 3
Oct 2014 - Jul 2016
Dragon 3 focuses on exploitation of ESA, TPM and Chinese EO data for geo-science and applications development in land, ocean and atmospheric applications. The Programme brings together joint Sino-European teams to investigate 50 thematic projects

Education

This section lets you add any degrees or diplomas you have earned.
Bachelor of Science (B.Sc.), Geography
Dec 2010 - Dec 2013
Specialisation: GIS & Remote Sensing

Thesis: "Investigation of the ASAR BIOMASAR GSV
maps from 2005 and 2010 using optical satellite data at
different temporal and spatial resolution in Northeast
China” (in the framework of ESA Forest-DRAGON 3)
Friedrich-Schiller-Universität Jena
Master of Science (M.Sc.), Geoinformatics & Remote Sensing
Dec 2013 - Dec 2015
Thesis: "Modeling the Spatial Distribution of Hail Damage in Pine
Plantations of Northern Spain as a Major Risk Factor for
Forest Disease"

Certificates

Reporting with R Markdown
Python Step by Step: Build a Data Analysis Program
Intro to Statistics with R: Introduction
Exploratory Data Analysis
Aug 2015
Intro to Statistics with R: Student's T-test
Getting and Cleaning Data
Nov 2014
From GPS and Google Maps to Spatial Computing
Nov 2014
The Data Scientist’s Toolbox
Sep 2014
Introduction to Machine Learning
R Programming
Sep 2014

Sources

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