PhD GRANTS IN ARTIFICIAL INTELLIGENCE FOR SMART MOBILITY AT THE UNIVERSITY OF DEUSTO (BILBAO, SPAIN)

[Antonio David Masegosa Arredondo]

** Apologies for cross-posting **

** Please forward to anybody who might be interested. **

The University of Deusto invites applications for several PhD projects
to be performed in DeustoTech. Deusto Institute of Technology
-DeustoTech- (http://deustotech.deusto.es/) located in Bilbao (Spain),
is a Research Institute of the Faculty of Engineering at the University
of Deusto, and was created with the mission of promoting research and
postgraduate training in Information Technology and Communications (ICT)
through the participation in research projects of interest to society
and industry. DeustoTech is looking for promising young researchers in
the area of Artificial Intelligence. The positions are directed to
master graduates and they are intended to offer three years fellowships.

*Grant summary*

The grants will have duration of 36 months, with annual renewals. Each
12 months the performance of the doctoral student will be evaluated to
check if he/she achieves the PhD research goals stablished for the
period. The exact amounts awarded will be established by the University
of Deusto. In the last year call, the annual gross salary was 16,6380€
for the first two years and 17,827€ for the third year. The application
is open to worldwide research applicants.

*Research topics*

Deusto Smart Mobility (http://mobility.deustotech.eu/), is a
high-performance research team that aims to contribute to a more
sustainable, safe and fair mobility through Artificial Intelligence and
Pervasive Computing. Applicants should choose four of the following
topics and indicate their order of preference. You can find more details
about each of these topics in the next link:
https://docs.google.com/document/d/1e5Dj-Wk4Ta0pXge84hWURpDPRl8vw-pYSriR6lcyvY8/edit?usp=sharing


1.    Deep Learning and Explainable Artificial Intelligence for Mobility
Applications
Contact: Enrique Onieva (enrique...@deusto.es)
2.     Optimization methods for sharing mobility scenarios
Contact: Enrique Onieva (enrique...@deusto.es)
3.     Concept drift detection and adaptation methods applied to traffic
forecasting tasks
Contact: Enrique Onieva (enrique...@deusto.es)
4.     ML4PYMES: New AutoML methods for small and medium-sized companies
Contact: Juan Angarita (js.an...@deusto.es)
5.     New AutoML methods for the sustainability of the Food Industry
using non-conventional data sources
Contact: Juan Angarita (js.an...@deusto.es)
6.    Optimization methods for last-mile logistics problems
Contact: Jenny Fajardo (jenny....@deusto.es)
7.    Artificial Intelligence methods for next generation transport
management
Contact: Antonio D. Masegosa (ad.ma...@deusto.es)
8.    End-to-end design of optimization methods through differential
programming
Contact: Antonio D. Masegosa (ad.ma...@deusto.es)

*Requirements*

Candidates should have a first class or good 2.1 honours degree in
Software Engineering, Computer Engineering, Telecommunications
Engineering or Transport Engineering (other equivalent disciplines will
be also considered). An appropriate degree at Masters Level will be
mandatory in order to access to the PhD program (applicants finishing a
Master’s degree along this academic year will also be considered).
Proficiency in spoken and written English is desired; knowledge of
Spanish is not a requirement. To be eligible, candidates must become a
full-time worker at DeustoTech facilities. All qualified candidates will
be considered.

*Closing date*

April 24th, 2022 (applications will be evaluated in order of arrival).
The selected applicants will go through a second phase, for choosing the
final candidate. This second phase has a deadline May 6th.

*Further details*

Applicants must complete the following form with their e-mail, name,
research topic preferences and CV in order to formalise their
application and be evaluated.
Form: https://forms.gle/iZjoboGUh7D7BbBR6

[Antonio David Masegosa Arredondo]

9.    Evolutionary generative machine learning
Contact: Arka Ghosh (arka....@deusto.es)
10.    Addressing Class Imbalance in Automatic Machine Learning
Paradigm: An Generative & Meta-Learning Approach
Contact: Arka Ghosh (arka....@deusto.es)

[Antonio David Masegosa Arredondo]