PhD scholarship within Predictive Runtime Verification of AI-enabled Robotic Autonomous Systems

søknader: 3 søknader
visninger: 28 visninger
Frist: Frist: 01.12.202619 dager igjen

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Stillingsbeskrivelse

About the job

A PhD scholarship is available within Predictive Runtime Verification of AI-enabled Robotic Autonomous Systems at Norges miljø- og biovitenskapelige universitet (NMBU) in Ås.

Responsibilities

Conduct research in Predictive Runtime Verification of AI-enabled Robotic Autonomous Systems
Develop and apply formal methods and runtime verification techniques to AI-enabled robotic systems
Collaborate with interdisciplinary academic and industrial communities
Contribute to the development of safety assurance and risk analysis frameworks for autonomous systems
Engage in practical work with physical mobile robotic hardware, sensors, and simulation environments

Qualifications

A Master's degree or equivalent in Robotics, Computer Science, Cybernetics, Formal Methods, Applied Mathematics, or a closely related field
Both the average grade point and the Master’s thesis must be B or better in the Norwegian educational system
Proficiency in both written and oral English
Solid knowledge and background in at least two of the following fields: Formal Methods / Runtime Verification, AI / Machine Learning, Autonomous Systems / Control, or Software Development for Robotics (e.g., ROS/ROS 2)
Strong programming skills (Python, C++, etc.)
Eligibility for security clearance at a minimum level of "Secret" and any licences or approvals required under Norwegian export control legislation

Personal qualities

Curious, result-oriented, and highly motivated
Strong communication and cooperation skills
Solid interest in robotics
Good collaboration skills and an ability to join interdisciplinary academic/industrial communities

We offer

A PhD scholarship within Predictive Runtime Verification of AI-enabled Robotic Autonomous Systems
Opportunities to work with cutting-edge research in AI and robotics
Collaboration with interdisciplinary academic and industrial communities
Access to physical mobile robotic hardware, sensors, and simulation environments

How to apply

Applications must be submitted by 01.12.2026. For more information, contact David Anisi (main supervisor), Associate Professor, david.anisi@nmbu.no.

Krav

A Master's degree or equivalent in Robotics, Computer Science, Cybernetics, Formal Methods, Applied Mathematics, or a closely related field
Both the average grade point and the Master’s thesis must be B or better in the Norwegian educational system
Eligibility for security clearance at a minimum level of "Secret" and any licences or approvals required under Norwegian export control legislation

Ferdigheter

Formal Methods / Runtime VerificationAI / Machine LearningAutonomous Systems / ControlSoftware Development for Robotics (e.g., ROS/ROS 2)PythonC++

Lønn

580 000 kr per år

Estimert lønn. Kan avvike fra lønnen arbeidsgiver tilbyr.

Erfaring

senior

Spørsmål og svar

Hvem er Norges miljø- og biovitenskapelige universitet (NMBU)?

Ansatte: 2 310
Hovedkontor: Ås
Avdelinger: 3 steder: Sandnes, Ås
Bransje: Undervisning ved universiteter
Organisasjonsform: Organisasjonsledd
Org.nr.: 969 159 570

Kilde: Enhetsregisteret

Hvor mange stillinger har Norges miljø- og biovitenskapelige universitet (NMBU) lyst ut de siste 90 dagene?

Norges miljø- og biovitenskapelige universitet (NMBU) har lyst ut 19 stillinger de siste 90 dagene.

Se andre stillinger fra bedriften

Hvor mange lignende stillinger er lyst ut de siste 90 dagene?

Det er lyst ut 72 lignende stillinger de siste 90 dagene.

Hvor mange stillinger etterspør hver av ferdighetene i denne annonsen?

Ferdighetene avgjør hvor mange stillinger du i det hele tatt kan søke på. Søylene viser hvor mange stillinger som er lyst ut de siste 90 dagene for hver enkelt ferdighet i denne annonsen — ikke bare her, men på hele Autosøk. De lengste søylene er ferdighetene arbeidsmarkedet etterspør bredest, og de er verdt å løfte fram øverst i CV-en og søknaden. De korteste er nisjeferdigheter: de gir færre treff, men også færre konkurrenter når de først etterspørres.