The Laboratory for Renewable Energy Systems (LARES) of University of Zagreb, Faculty of Electrical Engineering and Computing is looking to recruit a team for the implementation of the project ”T-LOGIC”.
The team consists of:
- one young researcher (m/f),
- one researcher (m/f),
- one experienced researcher (m/f).
The project aims at establishing an autonomous virtual manager in vending machines logistics including vending machines, delivery, warehouse, and capacities.
The management system is targeted as a cloud-based platform, utilizing artificial intelligence and machine learning on historical data, a mixture of various optimization approaches such as heuristic, evolutionary and convex optimization (e.g. mixed-integer linear programming) with experimental verification on a large number of real local and international vending machines, delivery routes and warehouses. The modular approach enables flexibility and prompt application to a particular case while the predictive modules are coordinated to achieve additional and synergic cost savings, energy efficiency and greenhouse gasses reduction.
The project is carried out in close collaboration with industry partner INTIS, the producer of Televend vending machines products and services – a team of over 100 vending enthusiasts that want to carry the traditional vending business into the future of connected smart machines.
SUPERVISOR:
doc. dr. sc. Vinko Lešić (vinko.lesic@fer.hr)
Candidates’ profile:
Experienced researcher
The ideal Experienced Researcher candidate should hold a PhD or several years of experience in electrical engineering or computer science with a specific focus on convex optimisation or machine learning and a special interest for the application context of smart city and logistics. Alternatively, the candidate could hold a PhD or several years of experience in mathematics or physics with an interest or references in machine learning and optimisation.
The candidate should have expertise in some of the topics listed below:
- linear programming, quadratic programming, or explicit model predictive control,
- mathematical modelling,
- data science, sampling, filtering,
- machine learning and neural networks,
- MATLAB/Simulink programming and simulation skills,
- Python programming skills,
- back-end programming skills, SQL queries, API creation,
- teamwork, social and mentoring skills (with references),
- autonomy and self-drive (with references).
Researcher
The ideal Researcher candidate should hold few years of experience in electrical engineering or computer science with a specific focus on convex optimisation or machine learning and a special interest for the application context of smart city and logistics. Alternatively, the candidate could hold a few years of experience in mathematics or physics with an interest or references in machine learning and optimisation.
The candidate should have expertise in some of the topics listed below:
- model predictive control,
- mathematical modelling,
- data science, sampling, filtering,
- machine learning and neural networks,
- MATLAB/Simulink programming and simulation skills,
- Python programming skills,
- back-end programming skills, SQL queries, API creation,
- teamwork and social skills (with references),
- autonomy and self-drive (with references).
Junior researcher
The ideal Junior Researcher candidate should have a master’s degree or equivalent 5-year study diploma in electrical engineering or computer science with a specific focus on control, embedded electronics or programming, and with an interest for the application context of a smart building, smart city and logistics. Alternatively, the candidate could hold a degree in mathematics or physics.
The candidate should have expertise in the majority of the topics listed below:
- high GPA (also required for enrolment as a PhD candidate),
- automation and control skills,
- programming skills,
- mathematical modelling,
- MATLAB/Simulink programming and simulation skills,
- teamwork and social skills (with references),
- autonomy and self-drive (with references).
Additional bonus skills to give an advantage among other candidates:
- convex optimisation and model predictive control,
- data science, sampling, filtering,
- machine learning and neural networks,
- Python programming skills in addition to MATLAB/Simulink.
Job Details
Number of positions available under the Grant: 3
Contract duration under the Grant: somewhat less than 3 years
Main research field: linear programming, quadratic programming, model predictive control, mathematical modelling, data science, sampling, filtering, machine learning and neural networks
Type of contract: full time
Working hours per week: 40
Application Details
Envisaged job starting date: December 2020
Application deadline: 5 November 2020
How to apply:
Interested candidates are invited to submit a cover letter, a detailed CV, and the contacts of up to three references by e-mail to vinko.lesic@fer.hr and ivana.basljan@fer.hr. Shortlisted candidates will be contacted shortly after their application for the interview.
