Ph.D. Opportunity: Wildlife Remote Sensing/Multi-Species Population Modelling
Job Title: Wildlife remote sensing/multi-species population modelling
Location: University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Closing: Please contact Philip McLoughlin by November 30, 2022. M.Sc. preferred. Candidates should have publications in mainstream peer-reviewed journals and a GPA equivalent of A over the past two years of coursework.
Apply: Email CV and pdf copies of both undergrad and graduate transcripts. Email to philip.mcloughlin@usask.ca. Please write “Sensor System Ecology” as the subject line.
Description: USask recently acquired an Overwatch Imaging TK-7 sensor system for the purpose of enumerating moose, caribou, and other wildlife in the boreal ecosystem across Western Canada. The TK-7 system allows for imagery acquisition at drone-level resolution with aircraft altitudes and coverage (e.g., 5.3-cm resolution from 2500 m AGL, strip widths to 450 mf or colour, with 25 cm thermal co-boresighted images). In collaboration with a graduate student in the Department of Computer Science at USask, who is training a program to recognize large-mammal detections, the Ph.D. position will work on optimization for censusing wildlife including altitude, timing, and calculations of detectability. Work will include planning and executing survey flights, collaboration and contribution to the machine learning process, and publishing optimized census methodology. Further research questions are at the discretion of the candidate and could include analysis of apparent competition, habitat selection, and effects of landscape change on moose, caribou, bison, and other interacting species detected in up to 40 different sampling areas. This position is unique in that it offers the Ph.D. candidate the opportunity to revolutionize how large-mammal surveys are conducted. (At less than half the cost of flying a helicopter and observer crew, the TK-7 can collect imagery for censusing in strips allowing for sampling of large areas, up to 1000 km2, in a single day). We expect a high citation score on papers on this novel censusing method. The Ph.D. is fully funded commencing Jan 1, 2023 or May 1, 2023. This Ph.D. will complement the graduate and post-doctoral projects of several students, whom will be working together to promote a multidisciplinary project aimed at conserving wildlife and promoting northern food security.
The project is fully funded at $25,000 CAD per year; however, students will be expected to apply for internal and external scholarships, including NSERC PGS-D scholarships (if Canadian) to offset salary costs. Candidates eligible for NSERC graduate awards are preferred.
The successful student will have an opportunity to engage with a large lab working on related questions with respect to large-mammal population dynamics. There will also be opportunities to work collaboratively with a diversity of staff from the Ministry of Environment. Students can expect to publish outside of one’s own thesis topic as part of whole-lab research questions.
Evidence of familiarity with GIS programs, ungulate population dynamics, and programming in the R language is an asset.
Interested applicants should contact me asap by email (philip.mcloughlin@usask.ca), and be prepared to submit a current CV with copies of transcripts (unless a post-doc, in which case only a CV is needed). Website: http://mcloughlinlab.ca/lab/
Ph.D. Opportunity: Sensor System Ecology in Saskatchewan
Job Title: Sensor system ecology and conservation in the boreal forest
Location: University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Closing: Please contact Philip McLoughlin by November 30, 2022. M.Sc. preferred. Candidates should have publications in mainstream peer-reviewed journals and a GPA equivalent of A over the past two years of coursework.
Apply: Email CV and pdf copies of both undergrad and graduate transcripts. Email to philip.mcloughlin@usask.ca. Please write “Sensor System Ecology” as the subject line.
Description: The Animal Population Ecology Lab at Usask has acquired an Overwatch Imaging TK-7 sensor system for the purpose of enumerating moose, caribou, and other wildlife in the boreal ecosystem across Western Canada. The TK-7 system allows for drone-level resolution with aircraft altitudes and coverage (e.g., 6-cm resolution from 1000 m AGL). In collaboration with a graduate student in the Computer Science department who is training a program to recognize large-mammal detections, the Ph.D. position will work on optimization for censusing wildlife, including altitude, timing, and calculations of detectability. Work will include planning and executing survey flights, collaboration and contribution to machine learning process, and publishing optimized census methodology. Further research questions are at the discretion of the candidate and could include analysis of apparent competition, habitat selection, and effects of landscape change on moose, caribou, bison, and other species detected in up to 40 different sampling areas. This position is unique in that it offers the Ph.D. candidate the opportunity to revolutionize how large-mammal surveys are conducted. At less than half the cost of flying a helicopter and observer crew, the TK-7 can cover ground at double the pace. We expect a high citation score on papers on this novel censusing method. The Ph.D. is fully funded commencing Jan 1, 2023 or May 1, 2023. This Ph.D. will complement the graduate and post-doctoral projects of several students, whom will be working together to promote a multidisciplinary project aimed at conserving wildlife and promoting northern food security.
The project is fully funded at $25,000 CAD per year; however, students will be expected to apply for internal and external scholarships, including NSERC PGS-D scholarships (if Canadian) to offset salary costs. Candidates eligible for NSERC graduate awards are preferred.
The successful student will have an opportunity to engage with a large lab working on related questions with respect to large-mammal population dynamics. There will also be opportunities to work collaboratively with a diversity of staff from the Ministry of Environment. Students can expect to publish outside of one’s own thesis topic as part of whole-lab research questions.
Evidence of familiarity with GIS programs, ungulate population dynamics, and programming in the R language is an asset.
Interested applicants should contact me asap by email (philip.mcloughlin@usask.ca), and be prepared to submit a current CV with copies of transcripts (unless a post-doc, in which case only a CV is needed). Website: http://mcloughlinlab.ca/lab/