Inesc Tec Is Accepting Applications To Award 1 Research Grant For Msc Or Degree Holders

Organisation/Company INESC TEC Research Field Computer science » Informatics Engineering » Computer engineering Engineering » Electrical engineering Mathematics » Applied mathematics Researcher Profile First Stage Researcher (R1) Application Deadline 8 Nov ***:59 (UTC) Country Portugal Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 1 Dec Is the job funded through the EU Research Framework Programme?
Not funded by a EU programme Reference Number AE* Is the Job related to staff position within a Research Infrastructure?
No CALL FOR GRANT APPLICATIONS (AE*) INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within the scope of the Multiannual Funding of R&D Units *, with the reference UID/**, Funded by national funds through the Portuguese Foundation for Science and Technology (FCT), I.P. 1. GRANT DESCRIPTION
Type of grant: Research Grant (BI) General scientific area: COMPUTER SCIENCE,ENGINEERING,MATHEMATICS Scientific subarea: Electrical engineering,Applied mathematics,Informatics,Computer engineering Grant duration: 8 months, starting on **, with the possibility of being renewed until the end of the project.
Workplace: INESC TEC, Porto, Portugal Maintenance stipend: * ou ***, according to the table of monthly maintenance stipend for FCT grants ( ... ), paid via bank transfer.
Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and Annex II), up to a maximum limit of 50 of the monthly maintenance stipend.
The grant holder will benefit from health insurance, supported by INESC TEC.
2. OBJECTIVES:
Deepen scientific and technical knowledge in the field of edge computing and distributed artificial intelligence applied to energy systems, with a particular focus on the operation and control of distributed energy resources.
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Study methodologies forthe local execution of forecasting, optimization, and control algorithms on edge devices with limited computational resources.
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Develop skills in applying machine learning techniques and large/small language models on edge devices, including the assessment of performance, latency, robustness, and computational efficiency requirements.
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Investigate smart charging and electric vehicle flexibility management methodologies, including charging session forecasting, flexibility estimation, and support for smart charging strategies, potentially including Vehicle-to-Grid (V2G).
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Contribute to the implementation and experimental validation of Edge AI solutions using devices such as gateways, embedded systems, and electric vehicle chargers.
3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING:
Conduct a literature review and critically analyse the state of the art in Edge AI, distributed intelligence, edge computing, federated learning, and smart charging, with a particular focus on applications to energy systems and distributed energy resources.
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Study the computationaland functional requirements for executing algorithms on edge devices, taking into account limitations in processing power, memory, energy consumption, latency, connectivity, and robustness.
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Develop and test electric vehicle charging session forecasting algorithms, including the prediction of start times, duration, required energy, and flexibility availability.
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Explore the use of machine learning models and edge-optimized small language models, assessing their applicability in environments with limited computational resources.
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Experimentally validatethe developed solutions in representative scenarios, namely smart electric vehicle charging and flexibility management of distributed resources.
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Critically analyse the results obtained and contribute to the preparation of technical documentation, reports, and scientific publications related to the work carried out.
4. REQUIRED PROFILE:
Admission requirements: Bachelor's or Master's degree in Electrical and Computer Engineering, Informatics, Computer Science, Applied Mathematics, or related fields.
The awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions.
Preference factors: Knowledge of electrical power systems, with a particular interest in distribution networks, distributed energy resources, or electric mobility.
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Experience in software and API development, preferably using Python or equivalent programming languages.
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Knowledge of artificialintelligence, machine learning, or federated learning.
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Fluency in English (written and spoken).
Minimum requirements: Advanced knowledge of a programming language (e.G., Python, C++).
Basic knowledge of data analysis.
5. EVALUATION OF