Bolsa de Investigação: ARDITI-CLEAN-ATLANTIC-2019-001

Job: Research fellowship (Master Degree): Applying new technologies and remote sensing for marine litter detection 

Job/Fellowship Reference: ARDITI-CLEAN-ATLANTIC-2019-001

Main research field: Marine Sciences Sub research field: Marine Pollution

Job summary:
  • Research fellowship (Master Degree): Applying new technologies and remote sensing for marine litter detection
Job description:

Marine litter is a global concern affecting the oceans and the coast that impacts on marine organisms and ecosystems, threatens human health and safety, and causes economic losses and aesthetic problems.

Tackling marine litter demands a cross-sectorial and multilevel approach, involving public and private stakeholders as well as NGOs and society.
A number of international, EU, regional, MS and local initiatives have addressed/are tackling this global problem (e.g. MSFD, D10, OSPAR Regional Action Plans). However, consistent approaches to monitor, record, map and remove marine litter are needed and demand collaborative work and coordination.

The project CleanAtlanticTackling marine litter in the Atlantic Area, (EAPA_46/2016) aims to protect biodiversity and ecosystem services in the Atlantic Area by improving capabilities to monitor, prevent and remove (macro) marine litter. The project will also contribute to raise awareness and change attitudes among stakeholders and to improve marine litter managing systems.

Within the scope of this project, ARDITI (Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação) is seeking a research fellow with a Master degree in Marine Sciences, Oceanography, Environmental Sciences, Computer Sciences (or other field of relevance) with training and/or expertise in remote detection, imagery analysis and GIS to contribute and assist in the development, optimization and implementation of monitoring and assessment protocols of marine litter (beached and floating) combining field sampling and aerial imagery collected from RPAS (Remotely Piloted Aircraft System) platforms. The main goal is to employ RPAS platforms (i.e. drones) to collect aerial imagery that may be used to detect and quantify beached and floating marine litter, optimize operations and analysis using field-sampling data as baseline of comparison.

The successful candidate is expected to join MARE - Marine and Environmental Sciences Centre R&D Unit in Madeira (a founding member of the Oceanic Observatory of Madeira) at Quinta do Lorde Marina, Caniçal, Madeira, between January and February (2020). The research team of MARE-Madeira, has broad interests and formal training in marine biology and ecology, marine biogeography and invasion biology and their research is focused primarily on marine benthic communities and how these are shaped by environmental conditions and anthropogenic pressure.

Admission Requirements

The ideal candidate will have a Master Degree in Marine Sciences, Environmental Sciences, Computer Sciences or other field of relevance to the CleanAtlantic project and the execution of the work plan detailed below, as well as previous prior experience, including (but not exclusively):

  • Background and/or proven experience record in imagery analysis (e.g. segmentation and classification);
  • Familiar with Geographic Information Systems (GIS) and GIS software
  • Experience and knowledge in data management;
  • Familiar with Machine Learning strategies towards object detection in imagery;
  • Familiar with RPAS (Remotely Piloted Aircraft System) or UAVs (Unmanned Aerial Vehicles), including piloting and maintenance;
  • Demonstrated experience in Photogrammetry and relevant software tools;
  • Prior work on marine litter; 
  • Demonstrated ability to work independently
  • Fluent knowledge (spoken and written) of the English language.
  • Strong communication and collaboration skills.

Workplan

The selected fellow will assist in developing and participate in research activities (both in the field and in the laboratory) related to: characterizing and monitoring of floating and beached marine litter; optimized monitoring protocols employing RGB and multispectral imagery collected with RPAS (Remotely Piloted Aircraft System) platforms; validating of a marine litter distribution and dispersal model; assessing how floating marine litter as a vector of introduction to marine non-indigenous species (NIS). Specifically, the fellows’ work plan will include (but not exclusively):

  • Aerial imagery (RGB and multispectral) processing and analysis to detect and quantify marine litter;
  • To assist in the development and implementation of marine litter monitoring employing RPAS/drone(s);
  • ML and other relevant data compilation and GIS integration;
  • Preparation and maintenance of electronic and technical equipment (such as, Drones, Photographic equipment, sampling nets);
  • To prepare and participate in land-based and boat-based field work and surveys focusing on marine litter monitoring and characterization;
  • To conduct and assist in the triage and sampling of marine litter (i.e. ML and associated fauna characterization);
  • To participate in workshops, project meetings and other relevant and/or project related events;
  • To assist in the production and writing of technical and scientific reports within the scope of the project

Scientific guidance and workplace

The work will be coordinated by Dr. João Gama Monteiro and Dr. João Canning-Clode at the Madeira Unit of MARE - Marine and Environmental Sciences Centre, in Quinta do Lorde Marina, Caniçal, Madeira Island (Portugal).

Duration
6 months, possibly renewable (until the duration of the project)

Salary
ARDITI fellowship stipend (989,70€) and regulations are determined by ARDITI (https://www.arditi.pt/index.php/biblioteca-on-line/outros-documentos/category/19-bolsas-de-investigacao).

Evaluation criteria
The following evaluation criteria will be applied:

FS = QA + EP + IN
FS – Final Score

Sum of Qualifications, Experience and Interview scores (if applicable), to a maximum of 40 points
QA – Qualifications 

  • Maximum score of 10 points: Master Degree in a field of relevance (e.g. Marine Biology, Ecology, Oceanography, Remote Sensing, Computer Science).

EP – Experience

  • Maximum Score of 20 points as follows:

Description

Max. Score (Points)

· Background and/or proven experience record in imagery analysis (e.g. segmentation and classification);

3 Points

· Familiar with Geographic Information Systems (GIS) and GIS software

3 Points

· Experience and knowledge in data management;

3 Points

· Familiar with Machine Learning strategies towards object detection in imagery;

3 Points

· Familiar with RPAS (Remotely Piloted Aircraft System) or UAVs (Unmanned Aerial Vehicles), including piloting and maintenance;

2 Points

· Demonstrated experience in Photogrammetry and relevant software tools;

2 Points

· Prior work on marine litter; 

1 Point

· Demonstrated ability to work independently

1 Point

· Fluent knowledge (spoken and written) of the English language.

1 Point

· Strong communication and collaboration skills.

1 Point

                     TOTAL

20 Points

IN – Interview

  • Top three candidates may be invited for an interview (in person or remotely) if QA + EP scoring has a maximum difference of 2 points or lower. Maximum score of 10 points.

Selection panel
Dr. João Monteiro (MARE-ARDITI)
Dr. João Canning-Clode (MARE, IMAR, Smithosonian)
Dr. Marko Radeta (ARDITI)

Application contents

  • Motivation Letter (emphasizing the candidate’s adequacy to each evaluation criteria)
  • Curriculum Vitae 
  • Degree certificate

Announcement of the results
Via e-mail.

Application submission and deadline
All applications must be sent by e-mail to: Este endereço de email está protegido contra piratas. Necessita ativar o JavaScript para o visualizar. until 17h of the 31st of December 2019. The subject of the email should be: 
ARDITI-CLEAN-ATLANTIC-2019-001

Vacant posts: 1 
Type of contract: Information not available 
Job country: Portugal 
Job city: Funchal 
Job company/institute: ARDITI-MARE Madeira 
 
Application deadline: 31 de Dezembro 2019 
(The Application's deadline must be confirmed on the Job Description)

 

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