Free open Job in North, South & Central America Post-doctoral Fellowship in Perinatal Predictive Modeling at McMaster University at McMaster University

Recent new research funds from a Canada Research Chair position and Canadian Institutes of Health Research (CIHR) grants have become available for a post-doctoral position in the Departments of Obstetrics and Gynecology/Health Research Methods, Evidence, and Impact (HEI), to develop novel, advanced analytic methodology to build and validate novel predictive models of complex biological processes to study maternal and neonatal diseases to improve the health of women and infants. This fellowship will enable fellows to extend knowledge in their research areas, conduct successful interdisciplinary research projects and establish new peer networks.

Only an hour’s drive away both downtown Toronto and Niagara-on-the-Lake, McMaster University resides in the heart of Southern Ontario. Hamilton is a beautiful and vibrant city with stunning natural sites, a thriving arts & culinary scene, and a rich history to explore. McMaster, home to Cochrane Canada Centre and the GRADE approach, is a world-leader in Knowledge Synthesis; HEI is particularly renowned with a vibrant series of rounds and educational opportunities. Among over 18,000 universities, McMaster ranked in the top 1% of the World Universities Rankings. McMaster is Canada’s most research-intensive university, with a total research income of $379,900,000 (2017), averaging $434,700 per faculty member – more than double the national average!

The successful candidate will be supervised in a vibrant, collaborative environment by Dr. Sarah McDonald, holder of a prestigious Canada Research Chair, a perinatal clinical epidemiologist and a high-risk obstetrician. We seek highly motivated applicants having a Ph.D. in (Bio)Statistics, Computer Science, Clinical Epidemiology, or related field, who are  able to work both as part of a team and independently, and enthusiastic about working with large perinatal data sets. Superlative programming (SAS/R) and communication skills are required. Experience in obstetrics or neonatology is an asset.

Primary responsibilities involve designing and executing predictive modeling studies, analyzing data, supervising students, gaining experience writing publications and research grants. The position is tailored to the applicant’s career goals.

The appointment will be for 1-3 years dependent on candidate goals, fit and productivity. Salary is commensurate with qualifications.

Interested applicants should email (Subject: “PDF in perinatal predictive modeling”) each of the following, numbered and titled per italics below (Only complete applications will be considered):

  1. curriculum vitae, including complete list of all publications, separately a list of publications in non-predatory journals, grant funding, and awards, month/year of start and completion of degrees and current position (or, please indicate if currently unaffiliated)
  2. letter (expressing why you are interested and career goals),
  3. transcripts (for each degree, notarized, translated into English, as necessary)
  4. course descriptions and duration (for all statistical/epidemiological courses, including #hours),
  5. publications (3 key English ones),
  6. references (names, titles, both email and telephone #)
  7. within your email, please include:
    i) where you saw this posting
    ii) year of your PhD (or indicate “no PhD”)
    iii) a table showing # peer-reviewed English publications on which you are (full text only in non-predatory journals; not abstracts):
    – First author
    – Senior/last author
    – Co-author (except senior/last)
    iv) Amount (currency, total) of peer-reviewed funding as:
    – Principal Investigator
    – Co-Investigator

to:

Dr. Sarah McDonald

Canada Research Chair

Professor

Departments of Obstetrics & Gynecology, Radiology, and Health Research Methods, Evidence, and Impact
McMaster University

mcdonals@mcmaster.ca

We thank all applicants, only those selected for interview will be contacted.