international selection call for one doctorate position under the programme SAICT-45-2017 PTDC/EEI-EEE/32550/2017 - Smart Transformers for Sustainable Grids – funded by Fundação para a Ciência e a Tecnologia, in the form of an employment contract under an unspecified fixed-term work contract – in the framework of Decree-Law No. 57/2016, of August 29, regulations for hiring doctorates to stimulate scientific and technological employment in all areas of knowledge - RJEC), with the amendments introduced by Law No. 57 / 2017, dated July 19, also taking into account the provisions of Regulatory Decree No. 11-A / 2017, of December 29 and the Código do Trabalho  (Labor Code), approved by Law No. 7/2009, of February 12, in its current wording -  being the basis of the contracting the performance of a specific service, precisely defined and non-durable, with a view to performing the following functions:
- Computer implementation of theoretical models for application to the project. Controllers design, development of dedicated simulation programs, participation in the construction of a prototype and experimental validation;
- To work in close coordination with a research team dedicated to the project. Participate in all stages of the research project, proposing innovative solutions, accompanying the research team and promoting the scientific dissemination of the developed work;
- In collaboration with the research team, promote and prepare at least one scientific publication per year in 1st Quartile Scimago / Web of Science journals;
- Promote and prepare the submission of patents;
- Participate in the project meetings and in all the dissemination actions carried out within the scope of the project;

GROUP LEADER
Prof.Rui Maranhão Abreu
rui@computer.org
RESEARCH PROJECT/RESEARCH GROUP
Information and Decision Support Systems Group Website
https://idss.inesc-id.pt/
POSITION DESCRIPTION
-Research Project / Research Group Description:
It is well-known that Quantum Computing (QC) has the potential to solve complex problems efficiently in various domains and bring breakthroughs in science and technology. Nowadays, quantum applications span over algorithms addressing optimization problems, such as radiotherapy optimization, machine learning techniques, chemistry simulations, and modelling (eg., handling uncertainties when predicting events).  The development of QC is also driven by the urgent need of solving ever-complex and large-scale problems, which current (super)computers cannot solve, and QC comes right on time to bring revolutionary computational power to handle such complexity. 
Though QC hardware is still immature, as most of the existing QC systems can only handle a limited number of qubits and are affected by noise, this is the time for getting “quantum ready”.  This means that industrial and academic research institutions can use this time to learn QC, devise new quantum algorithms, and build QC communities.  Specifically, the Quantum for Software Engineering (Q4SE) project aims to establish the theoretical foundations of a QC infrastructure supporting all aspects of software engineering and enable the development of high-quality quantum applications that can assist developers throughout the entire development lifecycle.   
We regard Q4SE’s domain as a new sub-field of software engineering that we expect to see growing in the next years, levering quantum languages and frameworks, such as Q# and Qiskit, Consequently, Q4SE will become highly relevant for the software engineering community in the years to come. Currently, there is still no one leading any efforts on Quantum Computing in the Programming Languages and Software Engineering communities. 
-Job position description:
The ambition of this project is to develop radically new methods for automated program analysis of classical software applications. It is well known that program analysis is a prohibitive task. Dynamic program analysis of programs is not reliable since it is intrinsically affected by false negatives, in that issues in programs can be identified during testing only if the testing suite exploits them. Static program analysis is, from this point of view, a much more promising alternative because it has the potential of being sound; in other words, at least theoretically, static program analysis should be able to have no false negatives. The problem with static program analysis is that, since its findings are based on a mathematical model of each program under analysis, for such model to be sound it has to overapproximate the set of all the program states that the program can take at run time. 
Such overapproximation inevitably leads to false positives. Today’s programs are very large and complex, and depend on additional metadata associated with them, such as deployment descriptors and configuration files. In order to build a sound mathematical model of such a large and complex program, a static analyser has to sacrifice precision, which means that the number of false positive quickly becomes very high—so much so that developers refuse to use static analyzers because of the time wasted in filtering out false positives. In order to reduce the number of false positives, numerous solutions have been proposed, but they all boil down to increasing the model’s precision, which in turn reduces the scalability of the analyser. The main goal of the Q4SE project will be to devise new QC algorithms for static analysis of classical programs, taking advantage of the computational power of quantum computers to resolve the well-known scalability/precision dichotomy

GROUP LEADER
Prof.António Grilo
antonio.grilo@inesc-id.pt
RESEARCH PROJECT/RESEARCH GROUP
Website of the Computing Systems and Communication Networks, which encompasses the Communication Networks group.
https://www.inesc-id.pt/research-areas/computing-systems-and-communication-networks/
POSITION DESCRIPTION
-Research Project / Research Group Description:
The Next Generation IoT (NGIoT) is the evolution of the Internet of Things (IoT) to become more secure, safe, trusted and human centric. NGIoT will become offer advanced and tactile IoT interfaces to the end user, together with hyperconnectivity, edge computing, distributed Ledger Technologies (DLTs) and Artificial Intelligence (AI) to optimize information processing. 
Fifth generation (5G) is one of the main enablers, providing high capacity mobile communication to NGIoT edge components. Transmission speeds in the order of the Gbit/s and submillisecond latency will enabling resource heavy applications, intelligent distributed camera networks and autonomous vehicles. However, the current cloud based computation and data delivery model does not allow the required quality of service (QoS) guarantees to be efficiently harnessed, due to the number of hops of wired networks between the 5G base stations and the cloud, which leads to a significant increase in latency. 
Moreover, in a massively sensed world, forwarding all the data generated by devices directly to the cloud may consume the available bandwidth and lead to congestion. Therefore, it is necessary that processing be hosted near the devices, close to the source of the data, so that the high speed transmission of 5G can be utilized and data can be processed and filtered out by the time it reaches the cloud. 
This bringing down of computation, storage and networking services to the network edge opens up many new research areas of applying Fog Computing over the 5G cellular network architecture. One of these research areas is service placement and migration optimization, whereby computing resources are assigned to service instance modules in the edge, fog and cloud in such a way as to meet application requirements and maximize resource management efficiency.
-Job position description:
This task will be hosted by the Communication Networks group of INESC-ID. The task consists in performing innovative research on the fog placement and migration optimization schemes in the context of NGIoT. Road safety in autonomous vehicle scenarios is regarded one of the most promising and challenging applications, though other IoT application may also be addressed. Real-time requirements, coupled with the need of communicating high data rates, clearly demand edge/fog computing solutions.
The accepted fellow will depart from existing related work on fog/cloud resource management and research innovative schemes that surpass or significantly improve the latter. Iterative optimization methods and genetic algorithms have been frequently used in related work, including previous work by our group. 
In order to become more scalable, two additional techniques will be investigated and possibly integrated:
 Hierarchical partition of edge/fog resources and with algorithm instances running in
parallel at the same and different levels.
 Optimization based on Machine Learning techniques. This consists of using tools such as deep neural networks to capture the characteristics of efficient resource allocation patterns, in order to spawn similar solutions in in similar situations. 
This technique should provide faster responses, once the neural network is trained. Our group has expertise and previous experience on 5G resource management, IoT and fog computing performance modelling. Regarding simulation tools, our group has already developed significant extensions to iFogSim [1], a state-of-the-art simulation tool relating IoT, cloud, fog and edge computing. However, this does not preclude the analysis of other simulators regarding their suitability to support this particular project.
The accepted fellow is expected to enroll in the PhD Programme in Electrical and Computer Engineering or the PhD Programme in Computer Science and Engineering at Instituto Superior Técnico – Universidade de Lisboa.