Call for Postdoc Position at UNIFESP, São José dos Campos, Brazil

Postdoc position in Operations Research and Machine Learning at the Federal University of Sao Paulo. The closing date for applications is 30th July 2021.

The Methods and Models of Optimization (MeMO) laboratory at Federal University of Sao Paulo opens a post-doctoral research position in Operations Research applied to “Models and Methods to solve the Patient Bed Assignment Problem and the Operation Room Scheduling Problem”. The selected candidate will work at the Institute of Science and Technology (ICT-UNIFESP), Sao Jose dos Campos, SP/Brazil.

The objective of this project is to investigate and develop exact and heuristic algorithms for two optimisation problems found in hospital management: Patient Bed Assignment Problem and Operation Room Scheduling Problem. We are interested in investigate practical constraints and patient preferences. The Biased Random-Key Genetic Algorithm (BRKGA) and Machine Learning techniques will be used to solve these problems.

Sao Paulo Research Foundation (FAPESP) provides the financial support according to and Financial support can also be provided to cover transportation expenses as to the move to Sao Jose dos Campos – Brazil. An extra grant is also provided to cover participation in highly relevant conferences and workshops, as well as research trips (limited to 15% of the annual amount of the fellowship). The position is for two years.

MAIN PROJECT: Development of a Hybrid Metaheuristic with Adaptive Control Flow and Parameters

SUPERVISOR: Antonio Augusto Chaves

DESCRIPTION: The study of efficient metaheuristics to solve optimization problems has been the subject of much research by the scientific community. To obtain good results in terms of solution quality and computational time it is important to have a good configuration of the metaheuristic. This process of specifying control flow and parameter values of a method is a di cult task. Hence, this project has as its main idea the development and improvement of the adaptive Biased Random-key Genetic Algorithm (A-BRKGA) method to choose which components will be used and in which sequence (A-BRKGA flow) and which parameters to use while an instance of a problem is being solved. To this end, machine learning techniques and adaptive and reactive mechanisms will be studied to construct an A-BRKGA with online configuration of parameters and control flow. The goal is to generate an efficient algorithm to solve combinatorial optimization problems and make the code easy to reuse. In order to evaluate the proposed method four optimization problems with industrial and logistical applications will be studied: field technician scheduling problem, multicommodity traveling salesman problem with priority prizes, two-stage capacitated facility location problem and facility location problem with overlapping. The computational tests will use available instances in the literature and real case studies. The method will be compared with state-of-the-art algorithms through statistical analysis.

REQUIREMENTS: Applicants should have PhD in Computer Science, Operations Research or in a related field with some experience with the C++ language, metaheuristics and machine learning. Candidates must have got their PhD in the last 5 years.

APPLICATION: The candidate must send by email until July 30, 2021 the following documents:

* Curriculum vitae with a list of publications, and previous experience.
* A recommendation letter from a previous supervisor/professor.
* A motivation letter for the application.

All documents must be sent to Prof. Antonio Chaves ( using ‘Post-doctoral application’ as the subject.