Organization: UNIVERSITY OF TORONTO
Deadline:Â September 24 2021
The FastPheno project is soliciting applications for several postdoctoral and fully funded grad student positions. The project aims to develop and use the next generation high-throughput drone-based phenotyping platform and observe forest dynamics, identify trees resilient to climate change impacts and to ensure future forest health and productivity. The positions are part of a large project with researchers from the University of Toronto, Université Laval, Natural Resources Canada and the Ministère des Forêts, de la Faune et des Parcs du Québec.
We are looking to hire postdocs and grad students with complimentary skills and interests in remote sensing, plant biology, tree genetics and genomics, data sciences, forest ecophysiology, bioinformatics, and statistical modeling. Successful applicants will work closely in a collaborative environment in an interdisciplinary team of researchers of the four participating organizations.
Postdoc Positions
Qualifications
I. Postdocs (two positions) remote sensing of vegetation and ecophysiology
The postdoctoral fellows will take leads in the FastPheno project activities on drone-based collection and processing of hyperspectral and LiDAR data from multiple experimental field sites and forest stands located in Quebec and Southern Ontario.
PROFILE – Candidates must hold a PhD in remote sensing, plant biology, forestry, or a related field. Strong background in photosynthesis, ecophysiology, leaf traits, remote sensing and big data analysis and experience with machine learning algorithms is required. Experience with retrieval of plant physiological and structural information using hyperspectral or LiDAR information is an advantage.
II. Postdoc (one position) statistical modelling and integration of adaptive traits for genomic selection
The postdoctoral fellow will take leads in the FastPheno project activities on developing and validating the relationship between adaptive traits and drone derived data and refine prediction models of phenology from phenotype trait data.
PROFILE – Candidates must hold a PhD in bioinformatics, biostatistics, or a related field. Strong background in advanced bioinformatics, statistical genomics, and model-data integration is required. Experience with plant biology, molecular ecology, genomic selection models and machine learning algorithms is an advantage.
III. Postdoc (one position) integration and visualization of genotype and adaptive trait phenotype data
The postdoctoral fellow will take the leads in the FastPheno project activities aiming to implement a new data browser and visualization tool which integrates genomic resources and drone derived phenotype data.
PROFILE – Candidates must hold a PhD in bioinformatics, environmental informatics, computer science or a related field. Strong background in plant biology, database design, data visualization and experience with scripting languages (e.g., Python, Rcran tools, Matlab) is required.
All postdoctoral candidates must have received their PhD after January 2018. Candidates must have strong verbal and written communication skills, willingness to work independently and in a collaborative team environment, and proven capability to publish in peer-review journals.
PhD/MSc Positions
IV. Grad students (five positions) forest dynamics – growth, canopy structure and ecophysiology
The PhD students will investigate climate adaptation in trees and focus on specific leaf traits. This includes e.g., leaf optical properties, chlorophyll-a fluorescence, and photosynthetic pigments, which will be upscaled to the canopy scale by coupling with drone-derived hyperspectral and LiDAR data. Integration of these data will eventually allow to derive tree level structure and physiology from remote sensing.
PROFILE – PhD candidates must hold an undergraduate or master’s degree in plant biology, forestry, or a related field. Experience in one or more of the following areas: Plant physiology, ecophysiology, plant molecular biology, ecology. Experience or an interest in learning programming languages such as Python, Rcran tools or Matlab for the analysis of large data sets is an advantage.
HOW TO APPLY TO THE ABOVE POSITIONS
Potential applicants should send their CV, a list with the names and contact information of 2-3 references and a max. one 1 page cover letter in a single PDF file to ensmingerlab@utoronto.ca. Use the words FastPheno Application in the subject line of your email followed by the number (I. to IV.) of the position you are applying for. The deadline for submitting your application is September 17, 2021
For questions on individual positions or the overall project please email [email protected].
For questions and further information about the co-investigators and their research use the following email addresses and visit their websites:
- Ingo Ensminger: [email protected], http://www.utm.utoronto.ca/ensminger/
- Julie Godbout: [email protected], https://www.researchgate.net/profile/Julie-Godbout
- Nathalie Isabel: [email protected], https://www.genomiqueforestiere.chaire.ulaval.ca/?page_id=4428#
- Ilga Porth: [email protected], http://www.cef-cfr.ca/index.php?n=Membres.IlgaPorth?userlang=en
- Nicholas Provart: [email protected], http://provart.csb.utoronto.ca/
- Postdoctoral Researcher: Remote Sensing of Peatlands
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- NIAS Post Doctoral Fellow|Project Associates
- Research Associate (postdoc) in urban modelling, spatial data analytics and machine learning (f/m) – Ref 21-18
