PhD position in geo-computational exposure assessment modeling


https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs/phd-student-developing-a-model-of-geo-computational-exposure-assessment-for-studying-health-effects

PhD student: Developing a model of geo-computational exposure assessment for studying health effects of urban environmental risk factors in Exposome-NL (1.0 FTE)

Deadline: 30 October 2020

The environment we live in has a significant impact on our health, explaining an estimated 70% of the non-communicable chronic disease burden. A large number of environmental stressors may jointly affect our health. Leading scientists in Europe and the USA have formalized this perspective as the Exposome concept: the sum of all non-genetic drivers of health and diseases. Interacting with the genome, it defines individual health at different stages throughout the life course.

There is a need to scale up and generalize the different ways how the general external Exposome can be measured in terms of diverse (geo)data sources and (geo-)computational methods. Environmental factors have been modelled in different forms, including spatial fields (continuous value surfaces), spatial objects (discrete spatially bounded entities), events (spatially and temporally bounded entities) and networks (quantified relations between objects). In the past, approaches for different environmental stressors have been less systematic, ranging from simplistic point-in-polygon tests and buffer based approaches to network-based interaction measures on different computational platforms.

The ambition of this project is to generalize computational models over different sorts of data, spatial information concepts (field, object, event and network) and computing infrastructures (desktop-based, database, cloud-based, and distributed). This includes developing semantic models of the different sorts of environmental factors, data sources and problem sizes, as well as semantic models of the different geo-computational transformations needed for assessing exposure. It also includes an investigation into scalable computational infrastructures (such as parallelization and cloud computing) and prototypical implementations for deploying exposure models for large problem sizes.

We are looking for a colleague who has a completed Master’s degree in computational geoscience, data science, GIScience, computer science, or a related discipline, with a strong interest in environment and health. We prefer colleagues with experience in conceptual modeling of geographic information and GIS workflows. You will work in a multi-center study funded by the prestigious Gravitation programme of the Dutch research funding organization NWO. Exposome-NL is a 10-year research programme of multiple Dutch universities.

Best regards,

-- 
Simon Scheider

Human Geography and Spatial Planning
Universiteit Utrecht

Vening Meineszgebouw A
Room 6.16
Princetonlaan 8a
3584 CB  UTRECHT
The Netherlands

+31 30 253 2966

http://www.uu.nl/medewerkers/SScheider
https://questionbasedanalysis.com/

PhD studentship at the intersection of GIScience and movement ecology


Sonar or Magnetism: exploring bat migration using a data science approach
We are looking for a candidate for a fully-funded interdisciplinary PhD studentship at the University of St Andrews, funded by the IAPETUS Doctoral Training Programme:

This project is at the intersection of GIScience and movement ecology: student will use data science to explore how migratory bats use information from Earth’s magnetic field for navigation. The student will fuse satellite geomagnetic data from ESA’s Swarm constellation with bat tracking data and then apply movement analytics to investigate migratory patterns.
The project is supervised by Urska Demsar (University of St Andrews), Ana Basiri (University of Glasgow), Ciaran Beggan (British Geological Survey), Jed Long (Western University, Canada) and Kamran Safi (Max-Planck Institute for Animal Behaviour, Germany)

We are looking for a student with an excellent degree (preferably MSc), potential prior research experience and experience in coding in Python. The competition is open to UK, EU and international students. The funding covers 3.5 years including tuition fees, maintenance stipend and research expenses.

For more information see: https://udemsar.com/2020/11/09/a-fully-funded-phd-studentship-in-giscience-ecology/

DL for expressions of interest is 10 Dec 2020.

——————————————-
Dr Urska Demsar
Senior Lecturer in Geoinformatics
School of Geography & Sustainable Development
University of St Andrews, Scotland, UK
@udemsar, https://udemsar.com/

Co-Director of the Bell Edwards Geographic Data Institutehttp://begin.wp.st-andrews.ac.uk/
Associate Editor, International Journal of Geographic Information Sciencehttps://www.tandfonline.com/toc/tgis20/current

Postdoc position @ UIUC’s CyberGIS Center


UIUC’s CyberGIS Center is recruiting a postdoctoral fellow to play key roles in research and education activities of multiple projects related to spatial modeling and simulation, geographic information science and systems (GIS), cyberGIS, machine learning, and/or geospatial approaches to solving public health problems. The application deadline is August 15, 2020https://cybergis.illinois.edu/career/postdoctoral-researcher-july-21-2020/. Your help for spreading the word would be greatly appreciated.

Call – professorship in Geoinformatics – Institute for Geoinformatics, Münster, Germany


Professorship (W2) in

Geoinformatics for sustainable development

starting at the earliest possible date.

Who are we looking for?

Applicants should have an outstanding PhD in Geoinformatics or a related field. They should have a proven track record (peer reviewed publications, international network, successful research funding acquisitions) in at least one innovative area in geoinformatics (GI) and in at least one of the many application areas of sustainable development research. Of particular interest are the following areas of research:

• Artificial Intelligence / Deep Learning / Data Mining in GI

• Sensor Networks / Internet-of-Things in GI

• Big Data in GI

• Spatial/spatiotemporal Network Modelling / Network Planning in GI

• Data Science in GI

• Geographic Information Systems / Science

• Remote Sensing, Time Series Analysis

• Geo-Visualization, Visual Analytics

• Trajectories, Movement Data or 3D Point Clouds

and the following application domains:

• GI for Sustainable Development Goals

• Smart Cities, Urban Development, Sustainable Mobility

• Renewable Energy, Energy Transition

• Climate, Ecology, Developing Countries

The applicant‘s track record should complement existing research areas at the University of Muenster, in particular at the Faculty of Geosciences and at ifgi.

Applicants will be required to contribute towards teaching (i.e. courses related to Geoinformatics that are part of several degree programs at the department). The teaching load associated with the advertised post is nine hours per week during the teaching terms. Courses are taught in English or in German. In addition, the successful applicant will supervise undergraduate and graduate students (including PhD students) and contribute to the self-administration of the institute and the department. When appointed, candidates will be expected to establish a research agenda and their own research group at ifgi.

The University of Münster is an equal-opportunity employer and is committed to increasing the proportion of female academics. Consequently, we actively encourage applications by women. Female candidates with equivalent qualifications and academic achievements will be preferentially considered within the framework of the legal possibilities. We also welcome applications from candidates with severe disabilities. Disabled candidates with equivalent qualifications will be preferentially considered.

What are we offering?

The Institute for Geoinformatics is one of the oldest and largest of its kind in Europe. Research and teaching cover a broad range of topics in Geoinformatics. Core research areas at ifgi currently include:

analysis and modelling of spatiotemporal processes, interaction with spatiotemporal information, spatial cognition, geodata infrastructures, geosimulation, UAVs, geotechnologies in education and smart cities. The institute offers a dynamic, research intensive and very international environment with many opportunities for collaboration throughout the institute, the faculty and the university; the main language of the institute is English. ifgi is also well-connected nationally and internationally. Collaboration across groups and disciplines is explicitly encouraged and practiced throughout the entire institute. In addition, Münster city is not only one of the most liveable cities but it also hosts several SMEs as well as larger companies working in areas closely related to Geoinformatics.

How to apply?

Please submit your application with the usual documents (including a research and a teaching plan) as a single PDF via email by September 30, 2020 to the following address:

University of Münster

Dean of the Department of Geosciences

Prof. Dr. Harald Strauss

E-Mail: dekangeo@uni-muenster.de

Questions about the offered position can be directed to Edzer Pebesma (edzer.pebesma@uni-muenster.de), Angela Schwering (schwering@uni-muenster.de), or Christian Kray (c.kray@uni-muenster.de).

Post-doc position in the MIning MUlti-source and MUlti-modal geo-referenced information (MIMU) project


http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=125792

Innovative geographic knowledge discovery is becoming increasingly possible through the analysis of large-scale data, for instance made available by Earth observation projects leveraging satellites and remote-sensing, provided by ground-level sensors, or given as volunteered geographical information (e.g., geo-referenced multimedia contents posted online on social media platforms). However, problems that involve combining remotely-sensed data with volunteered geographical information are only now starting to be explored, and they still involve a number of practical challenges, e.g. related to appropriate content classification.

Over the recent years, data classification leveraging deep neural networks has also become increasingly popular. These methods have been reported to result in impressive performance gains, when applied to problems related to processing images or natural language. Within GIScience research, deep learning can also have several applications, that are only now starting to be explored (e.g., for improving land coverage analysis, spatial downscaling methods, and general classification tasks that involve combining different types of data).

Within the MIMU project (i.e., acronym for MIning MUlti-source and MUlti-modal geo-referenced information), the researcher that is to be hired will work on the use of machine learning approaches for the discovery and mapping of innovative geographic knowledge through the analysis and processing of large-scale volunteered data (e.g., geo-referenced multimedia contents such as images and textual descriptions, posted on social-media platforms like Flickr, Twitter, or Foursquare), in combination with more traditional sources (e.g., remote- sensing products available in the context of initiatives like ESA’s Sentinel/Copernicus programme, and/or tables with socio-demographic data made available by statistical offices). The complex relations between the different types of information, as well as the temporal and geographical dimensions of the data, introduce new challenges that will explored throughout the project, in an attempt to go beyond the current state-of-the-art.

Krátkodobá brigáda pro studenty i stálé pracovní místo pro absolventy GIT a IT (ACCENDO Ostrava)


Výzkumný ústav ACCENDO – Centrum pro vědu a výzkum, z.ú. nabízí možnost krátkodobé brigády pro studenty i stálé pracovní místo pro absolventy v oblasti prostorových analýz, geoinformačních systémů a IT.

  • Firma pracuje na výzkumných projektech i na státních zakázkách, jejími klienty jsou obce, města, kraje, ministerstva, Úřad vlády.
  • Nástupní plat od 130 do 180 Kč/hod. s možností platového růstu.
  • Práce v mladém a vyvíjejícím se kolektivu, možnost odborného růstu
  • Prace po celé ČR i v zahraničí. Sídlo firmy Moravská 758/95, 700 30 Ostrava – Hrabůvka
  • Možnost spolupráce ihned.

Požadavky na zájemce: znalost práce v ArcGISu, Word, Excel, PowerPoint – pokročilý. Tvorba mapových a kartografických výstupů., příprava a zpracování dat.

V případě zájmu kontaktujte Ing. Ivanu Foldynovou, Ph.D., zástupkyni ředitele.

Tel.: 733 343 248, e-mail: ivana.foldynova@accendo.cz

Bližší informace o činnosti výzkumného ústavu ACCENDO zde: http://accendo.cz/

Post-doc position in the MIning MUlti-source and MUlti-modal geo-referenced information (MIMU) project


Post-doc position in Portugal 

http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=124415

Innovative geographic knowledge discovery is becoming increasingly possible through the analysis of large-scale data, for instance made available by Earth observation projects leveraging satellites and remote-sensing, provided by ground-level sensors, or given as volunteered geographical information (e.g., geo-referenced multimedia contents posted online on social media platforms). However, problems that involve combining remotely-sensed data with volunteered geographical information are only now starting to be explored, and they still involve a number of practical challenges, e.g. related to appropriate content classification.

Over the recent years, data classification leveraging deep neural networks has also become increasingly popular. These methods have been reported to result in impressive performance gains, when applied to problems related to processing images or natural language. Within GIScience research, deep learning can also have several applications, that are only now starting to be explored (e.g., for improving land coverage analysis, spatial downscaling methods, and general classification tasks that involve combining different types of data).

Within the MIMU project (i.e., acronym for MIning MUlti-source and MUlti-modal geo-referenced information), the researcher that is to be hired will work on the use of machine learning approaches for the discovery and mapping of innovative geographic knowledge through the analysis and processing of large-scale volunteered data (e.g., geo-referenced multimedia contents such as images and textual descriptions, posted on social-media platforms like Flickr, Twitter, or Foursquare), in combination with more traditional sources (e.g., remote- sensing products available in the context of initiatives like ESA’s Sentinel/Copernicus programme, and/or tables with socio-demographic data made available by statistical offices). The complex relations between the different types of information, as well as the temporal and geographical dimensions of the data, introduce new challenges that will explored throughout the project, in an attempt to go beyond the current state-of-the-art.

Open position for a lecturer (senior scientist) in GIScience at Department of Geography, University of Zurich


We have an open position for a lecturer position in Geographic 
Information Science at the Department of Geography at the University of
Zurich.

We’re looking for a motivated researcher with a proven ability to carry
out and publish independent research in a field relevant to our group’s
core research strengths. These include Geographic Information Retrieval
and the use of natural language, be it sourced from user generated
content, unstructured text or citizen science. We aim to develop
theoretically grounded, societally relevant approaches, which broaden
access to geographic information and allow decision making to better
account for diverse ways of understanding the world, with a particular
focus on the natural environment.

To find out more about the position go to this link:
https://apply.mnf.uzh.ch/positiondetails/5799936

If you have any questions, don’t hesitate to contact me.

Best wishes,

Ross Purves

Prof. Dr. Ross Purves
Department of Geography
University of Zurich

ross.purves@geo.uzh.ch