My department at @polymtl seeks to hire a tenure track Prof in Computational Fluid Dynamics #CFD #academicjobs
polymtl.ca/carriere/en/of…
Eight reasons you should apply: 1/9
Teaching is done in French. Research is done French or English. Speaking French is not a requirement for hiring. We'll get you a private French tutor and reduce your teaching load to help you become fluent!
2/9
Tenure and promotion are fast as compared to elsewhere in North America. Tenure is normally granted after four years.
3/9
Light teaching load, good startup package, support from senior faculty members. You will join a critical mass of researchers in experimental, theoretical and numerical fluid dynamics. We want you to succeed.
#apsdfd @aiaa @CASInstitute @csme2021
4/9
@polymtl has many agreements with the best universities around the world for student movement. Notably, we get an influx of graduate students from the best "Grandes Écoles" in France. You'll get to recruit among the best students!
5/9
30% of accepted students this fall semester at @polymtl are women. There is still work to do to achieve parity. We need more women professors! @AeroWomen
6/9
You will have access to world class computing facilities via @CalculQ and @ComputeCanada!
7/9
Montreal is a great city to live in and to raise a family. Housing is affordable. Daycare is highly subsidised. A 1-year parental leave can be split between both parents. I personally took 2 four-month paternal leaves during my tenure track period. #WorkLifeBalance
8/9
You will benefit from a great research ecosystem with plenty of opportunities for funding through industrial partnerships @AeroMontreal @CRIAQ_aero and for fundamental science as well @NSERC_CRSNG @FRQ_NT @InnovationCA
9/9

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More from @FrdrckGssln

19 Aug
Where should the operator shot peen a flat aluminium panel to form an aircraft wing skin?
Wassime Siguerdidjane, PhD student @polymtl, trained a neural network with FEM to do this!
doi.org/10.1016/j.mfgl…
This is akin to asking "How should the panel deform to adopt the wanted shape?" This is the inverse problem! Wassime's insight was to formulate it as a pattern recognition problem, for which Neural Networks are highly capable! #AI
Wassime coded a "maze generator" and its path finding algorithm. These path solution where then turned into random, yet realistic peening patterns. These 60,000 patterns were then solved by the Finite Element Method, forming training, validation and test data sets.
Read 7 tweets

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