RESEARCH FIELDComputer science › Computer systemsComputer science › CyberneticsComputer science › Database managementComputer science › Digital systemsComputer science › ProgrammingComputer science › Systems design
RESEARCHER PROFILERecognised Researcher (R2)Established Researcher (R3)
APPLICATION DEADLINE31/03/2020 17:00 - Europe/Brussels
LOCATIONNorway › Trollåsen
TYPE OF CONTRACTTemporary
HOURS PER WEEK40
OFFER STARTING DATE01/09/2020
EU RESEARCH FRAMEWORK PROGRAMMEH2020
REFERENCE NUMBERH2020-INNOSUP-2018-2020 (application under evaluation))
QBee AS is a Norwegian software company established in 2017, looking for a talented Artificial Intelligence/Machine Learning Engineer to join our team and help us develop automated anomaly detection features for our IoT device management platform. The prospect candidate will join QBee full-time through the European SME innovation Associate funding programme (INNOSUP-02-2019-2020). The fellowship is a fixed-term employment for 12 months with a competitive salary - starting in Q3 2020 - and a strong candidate will likely remain at Qbee after this period. The programme includes core EU training on innovation and entrepreneurial skills, while other competencies will be gained through our proprietary knowledge and external courses. At the time of recruitment, applicant must have not resided or carried out his/her main activity (work, studies, etc) for more than 12 months over the last three years in Norway. Recruitment is conditioned to the award of the INNOSUP-02-2019-2020 grant.
• Competitive salary can be expected (discussed on an individual basis).
• Relocation costs of the researcher and his/her immediate family (if applicable) to Norway
- Access to proprietary knowledge and tailored training
- Long-term employment at QBee following the project.
Application & Selection
1: Interested Candidates are required to email their CV along with a motivational letter (1 page) and at least one reference to contact to Carsten Lehbrink (email@example.com) before March 31st 2020 17:00h CET
2: Applications will be reviewed, and selected candidates will be shortlisted and notified by 30th April 2020.
3: Shortlisted candidates will be invited for a two-round interview process in late May / early June 2020, if the INNOSUP-02-2019-2020 grant is awarded.
4: Final selected candidate will be notified by 15th June 2020.
5: The candidate will be offered the research contract with a tentative starting date of 1st September 2020.
REQUIRED EDUCATION LEVELComputer science: PhD or equivalentEngineering: PhD or equivalentTechnology: PhD or equivalent
REQUIRED LANGUAGESENGLISH: Excellent
QBee is recruiting a highly motivated postdoctoral researcher to develop innovative algorithms based on unsupervised machine learning models to detect behavioural anomalies in embedded Linux devices. We are a start-up established in 2017 to develop and commercialize an innovative, highly secure and automated software platform to manage distributed multi-OEM IoT devices.
A research position is currently available to expand our team with an expert on state-of-the-art Artificial Intelligence (AI) techniques for a variety of datasets. Emphasis in on developing and applying unsupervised Machine Learning (ML) models to analyse embedded Linux devices state-based data for anomalies’ detection. The activities to carried out span all phases of AI/ML learning lifecycle, i.e. understanding the problem, data analysis and processing, multi-model training and evaluation, integration, testing and monitoring, in support of rapid deployment to benefit system integrators, operators, IoT device manufacturers.
We are now expanding our technical team, in order to develop these automated features that can help lower the risk of cyber-attacks on distributed IoT devices. Specifically, within a 12-month timeframe, we are looking for a researcher to:
- Develop ML algorithms, building upon published, state-of-the-art models to define baseline “signatures” and alert thresholds for various classes of devices
- Establish class-specific patterns for detection of anomalies and threats on IoT devices
- Test and classify the effectiveness of the algorithms established for both edge and cloud computing and selection of the best ones to integrate in the production platform.
Skills & Qualifications
- PhD in computational sciences, software engineering or similar
- Advanced programming experience (mandatory), preferably on Go language
- Minimum 2 full-time years of experience with TensorFlow (mandatory) or TensorFlow Lite (optional), developing, testing, debugging ML models
- Hands-on experience on development of ML models for anomaly detection in time series and unstructured data (preferable)
- Experience on configuration of edge computing and/or cloud systems (highly preferable); know-how in setting up and maintaining Linux based systems (experience in embedded Linux is a plus) using Ansible playbooks or similar; experience with remote secure connections (SSH, VPN) and REST APIs, preferably in industrial environment
- Strong communication and writing skills – fluency in English (mandatory).
Applicants must not have resided or carried out his/her main activity (work, studies, etc.) in Norway for more than 12 months in the 3 years immediately before appointment under the project.
EURAXESS offer ID: 467610
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