Open position for a Postdoctoral Researcher at Ca' Foscari University of Venice, Italy

The Department of Environmental Sciences, Informatics and Statistics at Università Ca' Foscari Venezia invites applications for a post-doc fellowship in implementation of the Precision Fish Farming approach in Mediterranean fish farms.
Ca' Foscari University of Venice - Italy
Application online, expiring on: September 4th 2020

The main goal of the research is the development and application of dynamic integrated models for implementing the Precision Fish Farming approach in managing rainbow trout (Oncorhynchus mykiss) in raceways and European seabass (Dicentrarchus labrax and seabream (Sparus aurata) both in land based system and cage culture.

Useful links: www.unive.it/data/28825/; www.unive.it/data/12137

Appointment and start date:  The initial appointment is 12 months and can be renewed, upon performance.

Salary: Net salary is about 2,400 Euros/month.

Location: Ca' Foscari University Science Campus, Via Torino 155, 30172 Mestre, Italy

Position Summary: The successful candidate will work on the development of dynamic models, including data assimilation algorithms, for implementing the Precision Fish Farming approach in the framework of two EU funded projects, GAIN https://www.unive.it/pag/33897  and NewTechAqua https://www.newtechaqua.eu/.

This novel framework combines process-based dynamic models with start-of-the-art non-invasive monitoring system for optimizing husbandry operations in aquafarms, in order to increase animal welfare, as well the environmental and economic sustainability of both land-based and marine fish farms. 

The candidate will be supervised by Prof. Roberto Pastres, coordinator of the GAIN project, pastres@unive.it

Essential Qualifications: Applicants should have a Ph.D. in quantitative disciplines, e.g. physics, applied mathematics, engineering, environmental sciences ..., and a solid background in the development and application of process-based dynamic models to aquaculture, other zootechnical sectors, or environmental systems. Knowledge of machine learning modelling would be appreciated. Applicants are expected to have strong expertise in coding, in particular using Python and R.