Cluster Hire in Remote Sensing Applications and Instrument Development at Northern Arizona University

Northern Arizona University is initiating a cluster hire of multiple faculty positions (including tenure-track and research-track positions) to build on NAU’s existing multidisciplinary strengths in remote sensing analysis that are critical components of many of its outstanding educational and research programs. The goal of this initiative is to increase NAU’s strength in the areas of instrument and application development to advance new techniques and instruments that address timely and critically important scientific questions across disciplines. Successful candidates will help to build remote-sensing capacity in existing areas of strength at NAU in engineering and the natural & social sciences, and/or will help to build new opportunities in novel disciplines. New hires will support the development of a research center focused on remote sensing. Areas of particular interest include, but are not limited to:
•       Space-based remote sensing of Earth and other planetary bodies.
•       Terrestrial, near-surface remote sensing (e.g., UAVs, tower- or tripod-mounted devices, hand-held instruments, wearable devices).
•       Software & analysis methods (e.g., autonomous devices, machine learning & AI applied to remote devices).
•       Instrument design and testing, including integration of off-the-shelf components for novel applications.

For minimum and preferred qualifications, as well as how to apply, please refer to the link below: https://hr.peoplesoft.nau.edu/psp/ph92prta/EMPLOYEE/HRMS/c/HRS_HRAM.HRS_APP_SCHJOB.GBL?Page=HRS_APP_JBPST&Action=U&FOCUS=Applicant&SiteId=2&JobOpeningId=607535&PostingSeq=1

Northern Arizona University is a committed Equal Opportunity/Affirmative Action Institution. Women, minorities, veterans and individuals with disabilities are encouraged to apply.

the U.S. Geological Survey are looking for a Physical Scientist / Geographer (https://www.usajobs.gov/job/759059600) to join our team at the Pacific Coastal & Marine Science Center in Santa Cruz, California.

Specific duties will include: 

• Use ArcGIS, MATLAB, Python, and Google Earth software for applications to ecosystems and climate change impact studies in coastal settings.
• Process and manipulate various complex geospatial datasets (e.g., remote sensing imagery, bathymetric data sets, sediment composition) to interpret geologic and oceanographic processes and delineate benthic habitats.
• Use ArcGIS and related software for applications for estuarine, coastal, and marine topographic, bathymetric, and sediment transport studies.
• Manage and co-author reports and maps to inform the scientific community of results of research.
• Work directly with editing and publishing groups to see products through the complete publication process.

The work will initially support our science on tropical coastal hazards and ecosystems across the globe, from the Mariana Islands in the western Pacific Ocean to the Virgin Islands in Caribbean. See these links for more information on the science questions and goals:

Coral Reef Project:

https://www.usgs.gov/centers/pcmsc/science/coral-reef-project

Coastal Climate Impacts Project:

https://www.usgs.gov/centers/pcmsc/science/coastal-climate-impacts

Physical demands:  

Field assignments involve outdoor work where there is considerable walking and climbing over rough terrain. The work required physical exertion such as lifting heavy objects over 50 pounds and working on vessels at sea.

NOTE: The applicant must be AAUS-quality scientific scuba diver certified.

Salary: $70,674 – $111,157 per year

Pay scale & grade: GS-9 to GS-11, depending on qualifications

Duration: This is a 13-month position, renewable up to 4 years.

Remote Job: No

Telework eligible: Yes — as determined by the agency policy.

Travel Required: Occasional travel — You will be expected to travel for this position.

Vacancy Announcement: USGS-SAC-24-12194948-DE-ML (will close Monday 11 December 2023)

The full job announcement and how to apply can be found here:  https://www.usajobs.gov/job/759059600

The Conservation GIS Lab (https://nationalzoo.si.edu/conservation-ecology-center) at the Smithsonian Conservation Biology Institute (SCBI) is seeking interns to start immediately. The internship project will focus on exploring the application of remote sensing space-borne lidar data (GEDI) and optical data (e.g., Sentinel or Landsat series) to classify vegetation types, particularly understory bamboo and woody invasive plants.

Learning objectives:

1. Pre-processing forest survey data and spatial datasets at a local or regional level
2. Extracting GEDI metrics using Google Earth Engine and Python or R
3. Building predictive spatial models for vegetation type classification using statistical analysis and machine learning algorithms.
4. Gain experience with the analysis of GEDI data, spatial data analysis, and scientific communication.

Qualifications:

1. A Bachelor’s degree is required, and a Master’s degree is preferred.
2. Commitment to a full-time schedule of 40 hours per week.
3. Strong background in programming (Google Earth Engine (required) and Python (preferred) or R).
4. Knowledge of GIS (preferably ArcGIS or QGIS) and remote sensing analysis is desirable.
5. Strong writing skills
6. Ability to conduct research independently and yet collegially in a team environment.

Benefits:

1. A competitive stipend of up to $1,600 monthly, based on education and experience.
2. Free dormitory-style housing on the SCBI campus.
3. Interns will have access to a computer and reliable high-speed internet.

Location & terms of appointment:

1. On-campus internship in Front Royal, VA
2. Minimum commitment of six months, beginning as soon as possible, with priority to longer-term candidates.
3. Engaging with a cohort of fellow interns and researchers through regular meetings and communications.

How to apply:

1. Applications are open until the position is filled.
2. Apply via the Smithsonian SOLAA system (https://solaa.si.edu/solaa/#/public). Search for “Internship” under “National Zoological Park”. The program name is “Conservation GIS Internship Program”.
3. Submit a CV and a cover letter detailing:

1)      Statement of Interest

2)      Potential start date and the duration of time you are available for the internship.

3)      Contact information for three references.

4)      Elaborate on your programming experience and skill as much as possible.

Note:

1)      Successful candidates may need a background check, which might include fingerprinting at a Smithsonian or police facility.

2)      Due to a high volume of applications received, we are unable to contact each applicant individually regarding the application status. The SOLAA application system will provide the most updated status regarding individual applications and the position.

About SCBI:

The Smithsonian Conservation Biology Institute is located at the north entrance of the Shenandoah National Park in Front Royal, VA, about 60 miles west of Washington, D.C. Dedicated to promoting the conservation of biological diversity, SCBI not only furthers the Smithsonian Institution’s broader objectives but also enriches our understanding by studying endangered species, habitats, and communities. Beyond research, SCBI plays an important role in sharing these insights through advanced studies, professional training programs, and public outreach.

Monitoring Scotland’s fresh waters from space: re-engineering the state-of-art using artificial intelligence

The studentship will be broadly structured into three inter-linked phases: (1) global model development and testing; (2) model validation for UK waters; (3) model fine-tuning and operationalisation – but with flexibility for the student to pursue other complementary research objectives as the project develops.

The studentship will capitalise on the wealth of existing data held by the Earth observation research group at the University of Stirling as well as community-owned databases (e.g., Limnades) and published datasets (e.g., Gloria). This will be augmented by in-situ monitoring data from SEPA and Scottish Water.

The ambition of this studentship is to fundamentally re-think how we use AI/ML methods for water quality mapping from space across the entire data processing pipeline, from pre-processing (e.g., atmospheric correction) through to the quality control post-processing, and possibly even for automating data interpretation. The student will work with experts in Earth observation and data science to re-engineer current approaches to the remote sensing of water quality with the prospect of the outputs of this research being embedded in future versions of the UK Lakes Observatory – our current operational water quality service. With support from key industrial partners, including the Scottish Environment Protection Agency (SEPA) and Scottish Water, this studentship has the potential to transform how we monitor Scotland’s rivers and lochs in the future.

Background:

Scotland’s freshwater ecosystems are under significant pressure from a range of stressors including inputs of diffuse pollution from agriculture, wastewater spillages from combined sewer outflows, and the increasing occurrence of hydroclimatic extremes (Marsden & Mackay, 2001; Krokowski, 2022; May et al., 2022). Identifying and understanding the effects of these pressures on water quality is pivotal to inform management and restoration strategies, to apprise future regulatory and policy frameworks, and ultimately to protect biodiversity and human health. However, organisations across the water sector, from government regulators through to water utilities, are finding it increasingly challenging using traditional methods to collect the data they need to evidence decision-making. What is more, the move towards net zero operations is forcing the industry to re-think its use of resources and to seek ‘smarter’ ways of collecting and using data.

Monitoring water quality at scale has always been a challenge. Conventional sampling programmes are resource intensive and, while networks of high frequency autonomous sensors hold much potential, high capital and maintenance costs remain a barrier to deployments at scale. Conversely, satellite remote sensing provides a relatively cost-effective means of collecting data on surface water quality over large geographical scales – while also providing observations at a frequency that enables both short-term events (e.g., storm-induced sediment resuspension, algal blooms) and longer-term changes (e.g., those driven by catchment land-use and hydroclimate change) to be resolved. However, the accurate estimation of water quality parameters (e.g., turbidity, chlorophyll-a) remains a challenge for applications over inland waters – particularly for smaller waterbodies only observable from platforms such as the Copernicus Sentinel-2 constellation (Palmer et al., 2015).

Much effort has been invested over the last decade in the development, validation, and fine-tuning of algorithms for the retrieval of water quality parameters from satellite data. Various solutions have been proposed from relatively simplistic empirical models to more complex physics-based solutions (Neil et al., 2019). Recently, there has been renewed interest in the application of artificial intelligence and machine learning (AI/ML) approaches to overcome some of the common limitations of more conventional solutions to the inversion of satellite data. Numerous AI/ML methods have been used to assist water quality mapping from space (Pahlevan et al., 2021; Werther et al., 2022; Yang et al, 2022), some with notable success but, in spite the ever-increasing research base, there remains little consensus on what AL/ML methods perform best and how to apply them.

FIND OUT MORE AND APPLY NOW: