Volume 23 No. 5

September 4, 2018

Employment Opportunities

PhD Position, University of Strathclyde, Glasgow, UK

The US Pacific Northwest (Washington and Oregon states) is one of the most biologically productive coastlines in the North Pacific, home to fish, seabirds, marine mammals, and human communities that all depend ultimately on the region’s intense phytoplankton blooms. At the same time, certain species of these phytoplankton (Pseudo-nitzschia spp.,) produce a potent neurotoxin (domoic acid) that builds up in shellfish and can kill humans and wildlife. The disruption to coastal economies can be intense. At present, we understand _where_ these toxic blooms originate (a handful of offshore hotspots), as well as the conditions under which wind-driven currents push them onto the beach, and on the basis of this have begun to produce a "Pacific Northwest Harmful Algal Bloom Bulletin" distributed to government and tribal resource managers to help with operational decisions (like when to close a beach to shellfish harvest). However, we are unable to predict _when_ toxin-producing species will appear in the offshore source regions. This is the area for potential improvement that this studentship targets: adding a biological component to our existing ocean-transport predictions, by a combination of statistical and mathematical approaches.

The student will be based at University of Strathclyde, Glasgow, UK, and co-supervised by Dr Neil Banas (http://neilbanas.com/projects/), Dr David McKee (http://www.strath.ac.uk/staff/mckeedaviddr/), and Prof Raphael Kudela (University of California Santa Cruz: http://people.ucsc.edu/~kudela/). Marine Science at Strathclyde consists of an active community of approximately 30 researchers and PhD students, and is embedded in the Marine Alliance for Science and Technology Scotland (MASTS) network. Tbe student will be encouraged to attend European and overseas conferences, join project meetings in Seattle, USA, and have the opportunity for extended visits to UC Santa Cruz and University of Washington, both of which are leading centres for marine science.

For more information and how to apply, please see here.

MSc Position, Redox-sensitive Element Geochemistry of Paleoproterozoic Red Beds, Memorial University of Newfoundland

seeking a motivated MSc student for a project investigating the mineralogy and geochemistry of Paleoproterozoic red beds in the Huronian Supergroup, Canada. The project will entail field logging and use of different mineralogical and geochemical techniques on samples to reconstruct the geochemical patterns of a full suite of redox-sensitive elements and the implications of their cycling during oxidative weathering and diagenesis. Many instruments for the projects are available at Memorial (e.g., in situ analysis using SEM, EPMA), but high-precision trace element and stable metal isotope geochemical analyses, when appropriate, will be undertaken abroad in Ireland, Germany, or both.

Applicants should be in excellent academic standing, eligible for Memorial University’s internal financial aid and external funding (e.g., NSERC PGS) support, have a strong sedimentology background, and have an interest in pursuing academic research in sedimentary/analytical geochemistry. Existing experience with ICP-MS and clean laboratory chemistry would be an asset, but those with a strong desire to develop such skills will be considered equally.

Interested applicants should email a statement of interest and CV to Dr. Michael Babechuk ( ) prior to official application to Memorial. Screening of applicants will continue until a suitable candidate is found, but a start time of spring 2019 is anticipated. Official application requires original transcripts, 2 letters of recommendation, and proof of English proficiency (www.mun.ca/become/graduate/apply/).

Memorial University is located in the vibrant and culturally fascinating seaside city of St. John’s, Newfoundland & Labrador (www.stjohns.ca) that offers ample access to hiking, whale/iceberg watching, and live music.

Wiess and Pan Postdoctoral Fellowships, Rice University, Houston, Texas

The Department of Earth, Environmental and Planetary Sciences at Rice University is inviting applications for the Wiess and the Pan Postdoctoral Research Fellowships. We are seeking candidates with independent research interests that intersect with one or more faculty within our department. Both domestic and international applicants are welcome, but applicants must have a Ph.D. awarded within three years of the time of appointment.

The research fellowships will be supported for two years, pending satisfactory progress during the first year, and covers an annual stipend of $60,000 with a benefits package and an additional annual discretionary research allowance of $3,500.

Applicants are requested to develop a proposal of research to be undertaken during the fellowship period. The principal selection criteria are scientific excellence, a clearly expressed research plan to address questions at the forefront of their field of study, and research synergies with at least one faculty. The proposed research should, however, encompass independent research ideas and explore new directions beyond the applicant’s Ph.D. Preference will be given to applicants whose proposals demonstrate independence and originality, and also the potential for collaboration with one or more faculty in the Department of Earth, Environmental and Planetary Sciences. 

The application for both fellowships is due on 1 November, 2018. Applicants are required to submit one application only at http://jobs.rice.edu/postings/15058. The application should include the following documents:
(1)     A cover letter.
(2)     A research proposal of no more than 3 pages (not counting references) of single-spaced text and figures.
(3)     A current CV, including a list of publications.

As part of the online application, the applicant will also have to provide the names and contact information of three or more people who will be asked to submit reference letters by the same deadline.

The highest ranked applicants will be invited to visit Rice in early 2019. Following acceptance, the appointment may begin anytime before 1 January, 2020. For further information or questions contact the chair of the search committee at esci-postdoc@rice.edu

Programs and Events

Python Workshop

When: 3-6pm on Tues Oct 9 & Wed Oct 10 

(the workshop is 6 hours in total, split into two afternoon sessions)

Where: ESB 5104-5106

Instructor: Jennifer Walker ( )

This workshop is an introduction to Python programming for scientific computing and research. Participants will learn basic programming concepts and syntax, gain a broad understanding of Python's rich ecosystem of scientific tools, and develop skills in data management and analysis using real-world scientific data.

The curriculum is designed for incoming graduate students with no previous programming experience. Any other students, staff, or faculty in the department who would like to learn about Python are also welcome to attend.

To register, please sign up here: https://goo.gl/forms/QJlUJ227m6NbOY9G2

Learning Goals

By the end of this workshop, participants will be able to:

  • Explain what programming is and identify possible uses for programming in their research.
  • Describe the Jupyter notebook structure and identify ways it can help make scientific research transparent and reproducible.
  • Create a Jupyter notebook including narrative text, equations, Python code, and graphs.
  • Identify the major data types in Python, work with variables, and perform basic mathematical operations.
  • Use loops and conditionals to automate tasks and control program flow
  • Explain what libraries are and identify some of the main libraries in the Python scientific ecosystem.
  • Use functions and data structures from the Pandas, Matplotlib, and Cartopy libraries for exploratory data analysis and visualization, including:
    • Read data from a CSV file and compute simple summary statistics and other calculations.
    • Extract subsets of a dataset using various indexing methods.
    • Use techniques such as sorting, filtering, and aggregation to explore a dataset.
    • Visualize data in graphs.
    • Plot geospatial data on a map.

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