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PhD Research Fellow in ICT
The University of Agder invites applications for one fixed-term appointment as a PhD Research Fellow in Information and Communication Technology for a period of three years. This position is currently located in Grimstad, Norway. The starting date is as soon as possible or to be negotiated with the Faculty.
The herein announced position will be part of a recently established Lab, namely, the Intelligent Signal Processing & Wireless Networks (WISENET) Lab, led by Prof. Baltasar Beferull-Lozano, and whose activities span across both the Department of Information and Communication Technology and the Department of Engineering. The WISENET Lab has a strong expertise in a range of areas, among them, Data Analytics, In-Network Processing and Distributed Intelligence, Wireless Communications, Networked Cyber-Physical Systems and Embedded Systems, having led a number of large research projects, funded by the Research Council of Norway, the EU research Programmes FP7 and H2020, as well as national and international industries. The WISENET Lab is now in full expansion phase, having at the present seven PhD students and three postdoctoral researchers working on different cutting edge research projects such as FRIPRO TOPPFORSK, SFI, PETROMAKS and INFRASTRUCTURE Projects, among others. The WISENET Lab is committed to achieving international research excellence, please see the notes about prospective PhD students at WISENET before applying.
The open PhD position will be offered in the areas of coordinated multi-variable data acquisition, intelligent data reduction, as well as automatic data quality verification and validation, advancing both theoretical aspects and algorithm designs, and considering also several application use cases, hence involving interdisciplinary research activities.
Research topics for PhD project
The deployment of cyber-physical networked systems in several industrial environments is generating tremendous streams of daily data in various formats and qualities, which describe the operation, condition, performance and status of a wide range of equipment. This represents not only an additional large volume of data to explore and the need for more efficient and scalable data analysis methods, but also raises additional challenges on real-time stream data processing, distribution, storage and machine learning methods, considering both centralized and decentralized Big Data analytics. Within this setting, an important example of application scenario is the one represented by the Oil and Gas (O&G) industry. Currently, managing and controlling operations in O&G industry are performed mostly remotely from on-shore data Centers. However, the future that is envisioned consists of having fully autonomous self-organized operations in both drilling rigs and production platforms, enforcing also a bi-directional cooperation between decentralized (real-time) off-shore intelligence and centralized on-shore intelligence. In this case, the role of on-shore Centers will consist mostly in supervising the autonomous operations, taking actions only if necessary in certain situations. In such vision, one of the fundamental issues becomes the intelligent acquisition and processing of the various heterogeneous data generated in these platforms, since this will drive the monitoring and automatic optimization of the key processes involved. This pre-processing of the data before it is shipped to an on-shore data Centre will enable a better real-time asset management, keeping continuous consistency between the high-level on-shore data analytics and the more granular off-shore intelligence. On the other hand, in some use cases of these application scenarios, there is a substantial percentage of the data that is not of good quality, which motivates also the need for an effective data quality verification. In addition, the databases and data processing that are mostly used nowadays cannot provide the necessary intelligence that is required to achieve this vision.
Your research will investigate new methods of smart collaborative data acquisition of variables describing the processes involved, including the associated coordinated distributed signal sampling to reduce the amount of acquired data while keeping the useful information to be extracted (important data patterns and trends related to the process), as well as an automatic quality verification and validation, taking into consideration also the dynamics of the process being monitored (e.g. states/transitions in sequential / in-loop processes), and the requirement of inferences that have to be obtained directly from the data to be able to: a) detect and predict situations, b) complete or correct other unavailable or erroneous data from some of the variables, and c) optimize the data acquisition towards the needs of process monitoring and automated control, which will be specified by the relevant metrics (e.g. energy efficiency, time efficiency or other performance criteria). The multimodal sampling will cover the set of variables defined for each of the processes, exploiting also their possible dependencies, and any data reduction or compression technique used to minimized the amount of data, will have to retain the key information that is needed for the specific requirements of further data processing and posterior analytics.
The main topics for the PhD position will be:
- Detailed definition of use cases to be considered, including the set of variables involved in the processes to be analyzed, as well as the required optimization metrics that should guide the novel methods for signal sampling, data qualification and verification.
- Detailed analysis of data quality issues in real processes for different type of actual sensors or virtual sensors: a) sensor fluctuations and noise caused by sensor itself, b) incorrect calculations in controllers before mapping to other layers, c) error propagation through different layers of filtering and mapping in current systems, d) wrong or inappropriate dead band, range or re-sampling rate for data reduction.
- Design of a novel data acquisition and processing architecture for optimized signal sampling, automatic quality verification and validation, structure of dependent data variables and data cleansing.
- Advanced techniques for data reduction techniques keeping the relevant information and understanding for a given process and a given expected inference/knowledge to be obtained. Some of the techniques to be explored will involve compressed sensing, matrix completion, sketch-based sampling techniques and sparse sampling over dependent variables, hence, exploiting also space-time dependencies of the data variables of the process.
Conditions and requirements
To be regarded as an eligible applicant, the candidate must have:
- A solid academic background with an MSc in ICT (or related) is required
- Substantial knowledge of all or most of the following:
- optimization techniques
- digital signal processing
- stochastic processes, statistical signal processing
- programming in Matlab, C/C++ and Python
- data acquisition systems in industrial environments
Moreover, it would be considered an advantage to have additional knowledge and experience in:
- Modern open-software Data Bases
- Data reduction methods or compressed sensing
- Working experience within Oil & Gas industry related to drilling systems or production
Candidates should also have:
- Scientific ambition.
- Motivation and strong interest in cutting-edge research.
- Good analytical and problem solving skills.
- Good team-working skills, inventiveness and a proactive attitude.
Participation in national or international projects related to these areas will be also considered as a plus.
In return, we offer the opportunity to contribute to the strategic capabilities of a world-class research organization, along with intensive PhD supervision. You will collaborate with top scientists in your field and have excellent prospects for personal development in an innovative working environment for aspiring researchers.
The PhD Research Fellow must be also admitted to the PhD Programme in Technology, within three months of appointment. More information about the programme and a complete list of admission requirements can be found here
The following admission requirements (established by UiA) apply to the PhD Programme:
- The average grade for courses included in the master's degree (or equivalent) should be B (or equivalent) or higher;
- The Master Thesis (or equivalent) should have a grade B (or equivalent) or higher when the candidate is admitted to the PhD program.
Further provisions relating to the position as PhD Research Fellow can be found in the Regulations Concerning Terms and Conditions of Employment for the Post of Post-doctoral Research Fellow, Research Fellow, Research Assistant and Resident.
Applicants from some countries must document their English proficiency through one of the following tests or certificates:
- TOEFL – Test of English as a Foreign Language with a minimum score of 550 on the Paper-based Test (PBT), or a minimum of 80 on the Internet based Test (iBT);
- IELTS – International English Language Testing System, with a result of at least 6.0
- CEFR (Common European Framework of Reference for Languages) certificate of at least Level B2.
Please check this website to see if an English test is required. Please note that the English test requirement applies to applicants from most countries according to the list mentioned above. No other English tests or certificates will be approved, and certifications/statements cannot replace an English test.
Short-listed applicants will be invited for interviews. With the applicant’s permission, UiA will also conduct a reference check before appointment.
The Norwegian public service is committed to reflecting the diversity of society, and the personnel policy of the University of Agder aims to achieve a balanced workforce. All qualified persons are therefore encouraged to apply for the position, irrespective of cultural background, gender, age or disability.
Women are especially encouraged to apply.
Appointment is made by the University of Agder’s Appointments Committee for Teaching and Research Positions. The successful applicants will have rights and obligations in accordance with the current regulations for the public service.
The position is remunerated according to the State salary scale, salary plan 17.515, code 1017, salary NOK 435 500 gross per year. A 2 % compulsory pension contribution to the Norwegian Public Service Pension Fund is deducted from the pay according to current statutory provisions.
Submit your application and CV online. Please click on the link “Apply for this job”. The following documentation should be submitted as attachments to the online application:
- Certificates and/or grades for all post-secondary education, up to and including the bachelor's level;
- Master's degree/higher degree certificate, with a summary of the courses/subjects included in the degree;
- Applicants with a foreign higher education must attach an official description of the grading system used at the issuing institution;
- Summary (approximately 1-2 pages) of the master’s thesis (if applicable)
- Applicants who are required to document their English proficiency must submit their TOEFL or IELTS test results (these may be forwarded after the closing date)
- Summary or links to the applicant's scientific/technical publications and patents (if any) and in addition, a hard copy of them should be submitted electronically in the application;
- A description of the candidate’s research interests, motivation and background for the project applied for.
The applicants are fully responsible for submitting complete documentation in a sufficient number of copies. Without complete documentation we cannot, unfortunately, include you in the assessment process.
Closing date: 27.04.17
For further information please contact Professor Baltasar Beferull-Lozano, e-mail firstname.lastname@example.org , tel: + 47 37 23 31 59 or Head of Department Professor Folke Haugland, tel. +47 37 23 32 20, e-mail email@example.com
In accordance with §25(2) of the Freedom of Information Act, applicants may request that they are not identified in the open list of applicants. The University, however, reserves the right to publish the name of applicants. Applicants will be advised of the University’s intention to exercise this right.
- Søknadsfrist27. april 2017
- ArbeidsgiverUniversitetet i Agder
- Intern-IDRef. 181/16
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