PhD positions (m/f/d) | Flora Incognita Max Planck Institute for Biogeochemistry

-




2 PhD positions (m/f/d) | Flora Incognita Max Planck Institute for Biogeochemistry


Job Offer from October 26, 2020
The Max Planck Institute for Biogeochemistry (MPI-BGC) in Jena is dedicated to interdisciplinary basic research in the field of Earth System Sciences with a focus on climate and ecosystems. The internationally renowned institute, which currently employs around 230 people, will celebrate its 25th anniversary in 2022. Jena is known for its high-tech industry, internationally renowned research facilities and a modern university. But it also has a beautiful natural setting in the green Saale valley with steep limestone slopes. The city of Jena has an active student scene and a diverse cultural life. In our Flora Incognita group we are looking for 2 PhD positions (m/f/d) limited to 3 years.
Background and position description:
Citizen Science approaches combined with latest machine learning are cutting-edge research topics embraced by the research group "Flora Incognita" at the MPI for Biogeochemistry. Our interdisciplinary team including botanists, computer scientists, physics, and media experts is working on transferring traditional plant identification into the digital age. In the long term, the data of the Flora Incognita app will enable us to investigate ecological and conservation issues. When do which species bloom? How much do morphological features of plant species vary? What is the relationship between plant occurrence and climate and land use change? Pressing questions of this kind will be addressed in two PhD projects starting early 2020:
Position I: Flora Incognita data for phenological modeling
Phenology is an important bioindicator of climate change. Flora Incognita provides us with important information about when, for example, plants are flowering. This PhD thesis will investigate to what extent Flora Incognita observations are suitable for phenological monitoring. Specific research topics are:
Comparison of Flora Incognita observations with high-level phenological observations
Integration of Flora Incognita observation data into process-based phenological models
New development of phenological models with new methods (e.g. deep learning)
Automatic image-based recognition of phenological states
Position II: Flora Incognita for species distribution modeling
Predicting species distribution plays an important role in many ecological applications and nature conservation issues. The PhD project aims to explore whether Flora Incognita data allow predictions of temporal and spatial species distribution patterns. Research topics are:
Integration of Flora Incognita observations into species distribution models at the level of single species and communities
Improving automatic recognition of plant species by integration of additional metadata (e.g. location and time)
Both doctoral projects combine the following key aspects:
analysis of high-dimensional ecological and environmental data with novel predictive methods
data integration across scales (e.g. in-situ and remote sensing satellite observations) and sources (e.g. crowdsourced vs. structured)
application of the latest machine learning methods in ecological modelling
The projects are carried out in close cooperation with the computer scientists at the chair of Prof. Mäder at the TU Ilmenau, with the "Biosphere-Atmosphere Interactions and Experimentation" group at the MPI-BGC (Dr. Mirco Migliavacca), and the "Earth System Data Science Group" (Prof. Miguel Mahecha) at the Leipzig University.
Your Profile:
Master degree (or equivalent) in Biology, Environmental Sciences, Remote Sensing, Computer Sciences, Applied mathematics, or any related field
Very good knowledge in statistics
Very good knowledge of at least one scripting language (e.g. R, Python, Julia)
Good communication skills in English and strong interest to work in an interdisciplinary research team
Our offer:
Become part of an internationally connected and renowned research environment. The conditions of employment, including upgrades and duration follow the rules of the Max Planck Society for the Advancement of Sciences and those of the German civil service. Remuneration follows in accordance with the TVöD public-sector pay grade 13 (65%). The Max Planck Society strives for equality between women and men and for diversity. It aims to increase the proportion of women in those areas in which they are underrepresented. Women are therefore expressly encouraged to apply. We welcome applications from all areas. The Max Planck Society has set itself the goal of employing more severely disabled people. Applications from severely disabled persons are expressly welcome.
Your application:
For further enquiries please contact Dr. Jana Wäldchen (jwald@bgc-jena.mpg.de). Please send your applications including a letter of interest, CV, copies of certificates and the names and contact information of two references by 30th November 2020 with reference number 26/2020 to:
Max Planck Institute for Biogeochemistry
Personalbüro: Kennwort "Flora Incognita"
Hans-Knöll-Straße 10
07745 Jena
Germany
or preferably as a coherent pdf document with the corresponding subject: bewerbung@bgc-jena.mpg.de. Please do not use any application folders, but submit copies only, as your documents will be destroyed in accordance with data protection laws after the application process is completed. We are looking forward to your application!





Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

Post a Comment

Popular posts from this blog

Spatial feature manipulation in remote sensing

-
Image
Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching
Read more

Denman Glacier Losing Some of Its Footing

-
Image
Denman Glacier Losing Some of Its Footing Using a combination of satellite sensors, scientists recently found that Denman Glacier has been retreating both above and below the water line. That one glacier in East Antarctica holds as much ice as half of West Antarctica, so scientists are concerned about its stability. From 1996 to 2018, the grounding line along the western flank of Denman Glacier retreated 5.4 kilometers (3.4 miles), according to a new study by scientists from NASA's Jet Propulsion Laboratory and the University of California, Irvine (UCI). The grounding line is the point at which a glacier last touches the seafloor before it begins to float. Behind the grounding line, the ice is attached to the bedrock; beyond it, glacial ice floats on the ocean as an ice tongue or shelf. The retreat of the grounding line at Denman means more of the glacier's underside is now in contact with water that could warm and melt it from below. If the grounding line continues to retreat,
Read more

Difference between Kriging and IDW

-
Image
Difference between Kriging and IDW. What does kriging mean? least squares estimate. Kriging is a type of regression that gives a least squares estimate of data (Remy et. ... Unlike linear regression or inverse distance weighted interpolation, kriging interpolation is based primarily on empirical observations, the observed sample data points, rather than on a pre-assumed model. What is GIS IDW? Inverse Distance Weighted (IDW) is a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. ... Specifying a lower power will give more influence to the points that are farther away, resulting in a smoother surface. Which interpolation method is best? The most used and promising techniques are universal Kriging and linear regression models in combination with Kriging (residual Kriging) or IDW. E.g.: Air temperature data – Kriging is most likely to produce the best estimation of a continuous surface, followed by
Read more