CALL-27-2019-1 - Researcher position in the Communication Technologies Division – M2M Department
The Communication Technologies Division of the CTTC is searching for interested candidates in a full-time fixed-term Researcher position for its Machine-to-Machine Communications Department in the context of the European-funded project FIREMAN. Detailed information about the project motivation and objectives can be found at the end of this position description.
TASKS AND RESPONSIBILIES
The principal objective of the position is to carry out cross-disciplinary research at the intersection of wireless connectivity for the Internet of Things (IoT) and machine learning towards end-to-end predictive and automated industrial systems. Specifically, the research will be oriented towards the completion of the following concrete tasks:
1. Retrieve the signal characteristics from the physical data captured/recorded using sensors and extract in-depth knowledge to instruct sampling/transmission schemes.
2. Model event-based traffic generation to accurately capture anomaly detection, service quality degradation, spatiotemporal dynamics, etc.
3. Design a data-driven, agile wireless network topology for the scalable deployment of a sheer scale of sensors with minimum connectivity overhead.
4. Optimize the wireless communication protocols to efficiently handle heterogeneous data transmission with stringent reliability requirements in industrial setups.
The position holder will be required to carry out research in the predefined areas and disseminate results through scientific publications in high-quality journals and top-tier international conferences. There is the possibility that part of the work involves close on-site collaboration with SEAT to facilitate access to data and process expertise.
The selected candidate will join the M2M Communications Department and will contribute to the group activities and discussions.
- A PhD (or close to completion) in Signal Processing, Data Science, Electrical Engineering, or closely-relevant field. Having a PhD thesis on a related topic is an advantage. It is desirable that the applicant has defended his/her doctoral thesis within the last five years. PhD students are also welcome to apply if their defense is scheduled for the next few months. The PhD thesis must be approved within the deadline for applying for this position.
- Solid understanding, project-based experience, and publication record in (a subset of) the following areas:
- Statistical signal processing and stochastic processes.
- Analytical modeling and machine learning techniques for data analysis in IoT networks.
- Wireless access protocols.
- Resource optimization in IoT networks.
- Excellent analytical and problem-solving skills.
- Programming skills in MATLAB, C++, Python or R.
- Scientific ambition, motivation and proactive attitude.
- Excellent communication and team-working skills.
- Strong academic credentials, written and spoken English proficiency.
- This is a full-time fixed-term contract aligned with the duration-frame of FIREMAN project with a total duration of approximately 2.5 years starting in Q1 of 2020.
- Position is based in Castelldefels, Barcelona (SPAIN) and physical attendance to the CTTC building is a must. No remote work is possible.
- Working hours are 37,5 per week and the candidate can enjoy 33 days of holidays per year.
- The call for applications will close on December 12th and the contract for the offered position will start as soon as the evaluation procedure is finalized. The end of the contract will be on April 30th 2022 at the very latest.
- The rank and salary will be determined according to qualifications and work experience, but it will be approximately within the range of 32-37 k€ yearly gross salary.
- CTTC offers a young and multi-cultural environment aimed at fostering excellent research and development activities, providing access to an outstanding research network, top-tech labs, and all tools needed to conduct top-quality research and development activities.
Interested applicants are required to submit online the following documents in English and in PDF format:
1. Full CV, including complete list of publications, link to the doctoral thesis (if any), relevant professional experience, awards and fellowships, and any additional scientific achievements or relevant merits.
2) Research statement (max 1 page) explaining the applicant’s qualifications and topics of preferred research focus and how the applicant’s competence can contribute to addressing the tasks associated with the position.
3) Contact information (email address, etc.) of two reference persons. We reserve the right to contact references only for selected candidates.
Applications are submitted online through this website. Please use the button at the bottom of the page to reach the application form. Incomplete applications or applications received by email will not be considered for the position.
Application deadline: Dec. 12th 2019.
Shortlisted applicants fitting best in the research profile expected for the position will be interviewed via tele-conference or on-site, whenever possible.
For further clarifications, please contact email@example.com.
ABOUT FIREMAN: PROJECT DESCRIPTION AND GENERAL OBJECTIVES
FIREMAN (Framework for the Identification of Rare Events via MAchine learning and IoT Networks) is a European research project funded under the CHIST-ERA scheme. The project is coordinated by Lappeenranta University of Technology in Finland and further involves CTTC and SEAT in Spain, University of Oulu in Finland, Athens Information Technology in Greece, and Trinity College Dublin in Ireland. The overall objective of FIREMAN is to design, develop and showcase a novel big-data based framework that encompasses all steps from sensing and data acquisition to statistical analysis and operational decisions, to accurately identify, detect, forecast and prevent rare events in a pre-determined industrial physical process. The proposed approach will be demonstrated on SEAT’s automotive manufacturing plant, as well as on University of Oulu’s 5G test network and Nokia’s base station factory in Oulu.
The next-generation digital manufacturing paradigm, known as Industry 4.0, calls for a massive deployment of smart sensors to monitor physical processes and improve manufacturing productivity, operational and cost efficiency. The vast amount of captured sensor data is generally heterogeneous and highly-dimensional; thus, data analysis and processing are necessary to transform multi-stream raw data into usable formats and timely detect/predict rare events in the industrial process (especially for condition monitoring and predictive maintenance on the industrial components). Rare events are situations of ephemeral and transient nature that occur infrequently. In this context, the currently installed fixed communication networks will eventually become incapable of effectively managing the ongoing industrial transformation. Therefore, industrial IoT systems will largely depend on advanced mobile wireless technology to satisfy key requirements in the advanced manufacturing paradigm, e.g., massive connectivity, ultra-high reliability, low latency and high availability. Based on the knowledge extracted from sensor data, a scalable networking architecture for large-scale data acquisition needs to be determined and a data-driven optimization framework for the sensors’ transmissions is required to achieve ultra-reliable connectivity. The design will rely on machine-learning techniques for i) the identification of rare events, e.g., faults and malfunctions, from the observed data and ii) the joint optimization of the underlying communication protocols for the optimal allocation of resources in time, frequency and space.
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), located in Castelldefels – Barcelona, is a growing and well-funded research institution fostering excellence and diversity. CTTC offers a highly international environment at an exceptionally attractive location. As a research center in telecommunications technologies CTTC provides a fertile environment for research cooperation and innovation between different disciplines. CTTC seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply. CTTC is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals.
CHIST-ERA (http://www.chistera.eu) is a program for European Coordinated Research on Long-term Challenges in Information and Communication Sciences and Technologies. It is a coordination and cooperation activity of national (and regional) research funding organizations mainly in Europe and is supported by the Future and Emerging Technologies (FET) program of the European Union through the ERA-NET funding scheme. Its aim is to reinforce the transnational collaboration between the participating states in challenging multidisciplinary research in the area of ICST with the potential to lead to significant breakthroughs.