Stipendiat i datateknikk, signalbehandling eller kybernetikk
Søknadsfrist 3. januar 2019
Universitetet i Stavanger
Universitetet i Stavanger (UiS) har omlag 12.000 studenter og 1.700 ansatte. Vi er eneste norske medlem av European Consortium of Innovative Universities. Universitetet har store ambisjoner. Vi skal ha en innovativ og internasjonal profil og være en drivkraft i kunnskapsutviklingen og endringsprosesser i samfunnet. Sammen med våre ansatte og studenter vil vi løfte blikket, og våge å tenke stort og nytt – vi vil utfordre det velkjente og utforske det ukjente.
Institutt for data- og elektroteknologi er en del av Det teknisk-naturvitenskapelige fakultet, og utfører forskning innen datateknologi, data science, kybernetikk og signalbehandling, og tilbyr bachelor- og masterutdanning innen elektroteknikk, datateknologi og data science, kybernetikk og robotteknologi og signalbehandling, i tillegg til PhD-utdanning innen informasjonsteknologi. Det er for tiden 50 ansatte, inkludert stipendiater, forskere og postdocs, og 600 studenter ved instituttet.
Universitetet i Stavanger har ledig stilling som stipendiat i datateknikk, signalbehandling eller kybernetikk ved Det teknisk- naturvitenskapelige fakultet, Institutt for data- og elektroteknologi.
Dette er en utdanningsstilling som i hovedsak skal gi lovende forskere anledning til faglig utvikling. Stillingen har forskerutdanning fram til doktorgrad som mål.
Stipendiaten ansettes for en periode på tre år med ren forskerutdanning eller fire år med forskerutdanning og 25% pliktarbeid. Dette blir avklart i rekrutteringsprosessen. Stillingen er ledig fra 1.4.2019.
Det er mulig å søke på opptil tre av følgende prosjekter:
- Deep learning for personal monitoring devices
- Modeling and simulation of antimicrobial resistance in microbial communities
- Geographical time series data mining for detecting slow-onset disasters
- Ultra-linear Digital-to-analogue Conversion
- Scaling atomic multicast through content-based addressing
Vennligst oppgi og ranger hvilke prosjekt du ønsker å jobbe med i søknaden din.
Søkere må ha en sterk faglig bakgrunn med femårig mastergrad, fortrinnsvis av nyere dato, eller tilsvarende utdanning som gir grunnlag for å gjennomføre en forskerutdanning. Karakter på masteroppgaven og veid gjennomsnittskarakter på masterstudiet må begge hver for seg tilsvare B eller bedre for å komme i betraktning.
Ved vurdering vil det bli lagt vekt på søkerens potensiale for forskning innenfor fagfeltet, samt vedkommendes personlige forutsetninger for å gjennomføre forskerutdanningen.
Den som ansettes må kunne arbeide selvstendig og i et fellesskap, være nytenkende og kreativ. Stipendiaten må ha gode ferdigheter i engelsk, både skriftlig og muntlig.
Stillingen anses som en viktig rekrutteringsstilling til vitenskapelig stilling ved universiteter og høgskoler.
Studiet gjennomføres i hovedsak ved Universitetet i Stavanger, bortsett fra et avtalt utenlandsopphold i et anerkjent relevant forskningsmiljø.
Stipendiaten lønnes etter Statens lønnsregulativ l.pl 17.515, kode 1017, kr 449.400 bto pr år. Stillingen gir automatisk medlemskap i Statens pensjonskasse som sikrer gode pensjonsrettigheter.
Prosjektbeskrivelse og nærmere opplysninger om stillingen fås ved henvendelse til:
- Instituttleder Tom Ryen, tlf 5183 2029, epost [email protected]
Opplysninger om ansettelsesprosessen fås ved henvendelse til HR-rådgiver Janne Halseth, tlf 5183 3525, epost [email protected].
Universitetet har få kvinner i rekrutteringsstillinger innenfor fagområdet og oppfordrer derfor spesielt kvinner til å søke.
Søknaden registreres i et elektronisk skjema på jobbnorge.no. Relevant utdanning og erfaring skal registreres i skjemaet. Vitnemål, attester, publikasjonsliste og ev annen dokumentasjon som du ønsker det skal tas hensyn til, lastes opp som vedlegg til søknaden i separate filer. Dokumentasjonen må foreligger på et skandinavisk språk eller engelsk. Hvis vedleggene overskrider 30 MB til sammen må disse komprimeres før opplasting.
Prosjektbeskrivelser og kontaktpersoner:
1. Deep learning for personal monitoring devices
Physical inactivity is a major challenge to global health and the problem is increasing rapidly: >5 million deaths each year are attributable to insufficient physical activity. Norway is among the European countries with the lowest level of spontaneous physical activity. Recent technological advances in personal heart rate and activity monitors in form of smart watches may make these devices potentially important tools for individualized guidance on physical activity. Despite the rapidly increasing use of smart watches there is as yet no long-term documentation for their benefit and their potential role as diagnostic tools has not been established.
Characteristics of heart rate and changes in heart rate during exercise and rest, are strong predictors of cardiovascular outcome. At the same time in the recent years, smart watches with integrated HR monitors for the first time became truly available to the average consumer. This makes meaningful research possible. Smart watches produce significant amounts of data, what calls for automated data analysis and requires application of big data tools in addition to a mix of other data science concepts such as machine learning and time series analysis.
The research will be performed primarily based on data obtained during the NEEDED 2014 and NEEDED 2018 study, containing heart rate (HR), power (W), ECG, blood samples, and other data for over 60 subjects collected during “Nordsjørittet”. Additionally, a mechanistic study was be performed during the spring of 2018 (NEEDED 2018), adding vast amounts of data on the relationship between heart rate, direct work measurement (powermeters), 12-lead ECG, and a large number of biomarkers, both during standardized physiological tests and during a bicycle race.
We aim to develop a model that predicts the expected, normal HR response to physical exercise in relation to biomarkers. This algorithm for the detection of a pathological heart rate response will be tested in future studies. A variety of methods will be explored ranging from basic feature engineering and classification, though time series analysis, to deep learning.
Supervisors: Associate Professor Tomasz Wiktorski, [email protected], Professor Trygve Eftestøl, and MD and Professor II Stein Ørn.
2. Modeling and simulation of antimicrobial resistance in microbial communities
This project aims to develop mathematical models to better understand and predict how antimicrobial resistance spreads in microbial communities. Of particular interest is the spread of carriers for antimicrobial resistance (genes) in wastewater treatment plants since such plants are nodal points for further spread of into the environment. Two approaches will be examined in this project. The first is to use deterministic ordinary differential equations. The plant, bacterial populations and resistance genes are treated as continuous concentration state variables, and production and degradation are modeled by pseudo-kinetics and conversion stoichiometries. The second is individual-based and stochastic. An individual based model (IBM) is one where bacterial classes (guilds and genotypes), genetic carriers (plasmids) and viruses (bacteriophages) are treated as individual and discrete populations.
The work in this project will be conducted in collaboration with a postdoc also working with the modeling and other PhD candidates working on experimental studies on the spreading and ultimate fate of antimicrobial genes in a laboratory scale wastewater treatment system. Experimental data will be used for systems identification and calibration/validation of the proposed model. The candidate will join a group who also work on a closely related EU-funded project under the Joint Programme Initiative on Antimicrobial Resistance (JPI-AMR) with collaboration from top international groups at Lund University (Sweden) and Statens Serum Institute, Copenhagen (Denmark).
Applicants must have a strong academic background with a master’s degree in one of the following: dynamical systems, mathematical modeling, control theory/engineering (kybernetikk), computational engineering, computer simulation, or other related fields. A background in biology is not required, necessary courses in biology will be offered.
Supervisors: Associate professor Kristian Thorsen, [email protected] and associate professor Roald Kommedal.
3. Geographical time series data mining for detecting slow-onset disasters
The main idea behind this project is to use spatio-temporal analysis, spatio-temporal data mining, and time series analysis to detect slow-onset disasters. A slow-onset disaster is a disaster that does not occurs suddenly but instead happens gradually, and there is hope of detecting it early. One example of a slow-onset disaster is an epidemic.
Data sets have already been acquired dealing with seasonal influenza outbreaks in Norway. This will be used as an example, with the goal of detecting the seasonal influenza earlier than the methods currently used by the Norwegian Institute of Public Health (NIPH). Currently, the NIPH detects an outbreak based on reported diagnoses from doctors. The doctors report this every other week, which means that there is a two-week delay from people starting to get sick to the NIPH knowing about it.
There is increasing monitoring in modern societies, and thus increased amounts of data that might be used to detect disease outbreaks earlier. The idea is to look for changes in how people behave, and particularly those changes that might indicate people being sick. One source of data that might be used for this is the number of cars that pass by automated road tollbooths or checkpoints from the road authorities that count the number of cars. A data set consisting of the number of cars passing a number of such checkpoints each hour is available and may be used for this purpose. Each checkpoint having a geographical position and associated time series. The flow of traffic is best measured by combining several time series at geographically distinct points in the same city.
In order to find interesting patterns of behaviour change, data from multiple sources likely has to be considered together as each source individually is noisy. Traffic flow may be affected by other things than epidemics, such as road works or major football matches. Examples of such other data sources are ticket purchases from public transport systems, utility usage in residential and commercial areas and purchases of medications. Social media may also be used as an information source.
This project will involve using state of the art data mining methods for spatio-temporal data mining and mining time series data. The project will also involve using machine-learning methods on graphs and spatial time series data.
The general research questions are as follows:
RQ1: How to best store and analyse geographical time series data. Develop algorithms for analyse big geographical time series data.
RQ2: How to combine data from the analysis of multiple sources from RQ1 in order to detect interesting changes in behaviour that might indicate a slow-onset disaster that is on its way.
RQ3: How to best visualize the results from the analysis from RQ1 and RQ2.
This project is a collaboration between the Department of Computer Science and the Centre for Risk Management and Societal Safety (SEROS). This project deals with the technical challenges in such a project while there is an ongoing project at SEROS dealing with the societal safety issues.
The candidate should have a master degree in computer science or data science, preferably with a background in data mining and/or geographical databases.
Main supervisor: Erlend Tøssebro, [email protected], Co-supervisor: Vinay Jayarama Setty
4. Ultra-linear Digital-to-analogue Conversion
You will be joining a project that aims to use control theory to develop methods for digital-to-analogue conversion (DAC), enabling a dynamic range better than 1 part-per-million (equivalent to 21 effective number of bits), at high speed and with low latency. This level of performance has never been achieved before and will define the new state-of-the-art in the field. A semiconductor device with such capabilities will be key to enabling technology in several areas of industry and science as it will allow mass-market availability of unprecedented precision enabling techniques, which was not previously possible due to signal noise and distortion. In addition, it will dramatically improve the performance in any device already reliant on high-resolution digital-to-analogue conversion.
Today, analogue-digital conversion is ubiquitous. Analogue-digital conversion devices sold for USD 3.5 billion in 2017, and that market is increasing 10-15% annually. Better linearity, and thus higher resolution, will benefit systems using analogue-digital conversion. It is a key element in systems used in science, industry, medicine and consumer goods. High-resolution digital-to-analogue conversion and control has a wide array of applications, including: adaptive optics; semiconductor lithography, fabrication and inspection; laser interferometry; metrology (measurement science); imaging and manipulation in microbiology; chemistry and materials science; as well as scanning probe microscopy in general. Furthermore, methods relating to analogue-digital conversion will have major impact in renewable energy production and distribution, medical imaging and communications.
The best performing digital-to-analogue converter (DAC) available today achieves a resolution of 47 parts-per-million, or 15 effective number of bits. The project supervisor has already set the new state-of-the-art by building a DAC with a resolution more than 12 parts-per-million (17 effective number of bits).
You will be building on these results and taking it further! The work is multidisciplinary, drawing on control theory, electronics and signal processing, though the main tool will be model predictive control (MPC). You should therefore have a good background or strong interest in control theory, optimisation and estimation (Kalman or particle filtering). The work can focus on experimental results in the lab or be more oriented towards theory, depending on your background and interests. The project involves international collaboration with researchers from The University of Newcastle (Australia), Aalborg University (Denmark), SINTEF and the Norwegian University of Science and Technology. You will be expected to work in one or more of these locations for up to 1 year.
Supervisors: Associate professor Arnfinn A. Eielsen, [email protected], professor Andrew John Fleming (The University of Newcastle, Australia.)
5. Scaling atomic multicast through content-based addressing
Atomic multicast is a fundamental building block for scalable distributed systems. Atomic multicast allows processes to reliably and consistently send messages to one or more groups of servers. A typical use case is a client broadcasting an update for a distributed object to a group of servers replicating that object. Atomic multicast allows updates to be applied consistently to multiple objects replicated by different groups. However, updates spanning multiple groups remain a challenge to the scalability of these systems.
Existing systems try to mitigate this cost by adapting the mapping of objects to groups to minimise the number of groups that process an update. This results in a complex and highly dynamic mapping of objects to groups.
Content-based addressing schemes, e.g. publish-subscribe, decouple senders and receivers in a messaging system.
The aim of this project is to investigate how content-based addressing schemes can be applied to further improve the scalability of atomic multicast systems, e.g. relieving clients and servers from the need to maintain complete address tables.
A special challenge is how content-based addressing can simplify dynamism in the assignment of distributed objects to groups of servers while maintaining correctness criteria.
Applicants should have a good understanding and experience building distributed systems.
Supervisors: Associate Professor Leander Nikolaus Jehl, [email protected], and Professor Hein Meling.