Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of Oslo
- University of Bergen
- NTNU - Norwegian University of Science and Technology
- University of South-Eastern Norway
- Western Norway University of Applied Sciences
- UiT The Arctic University of Norway
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- UNIS
- University of Agder
- University of Stavanger
- Østfold University College
- 1 more »
- « less
-
Field
-
Background in probabilistic methods Experience with the application of AI algorithms and probabilistic methods Good programming skills Personal characteristics To complete a doctoral degree (PhD), it is
-
/background. This statement of research interest should not exceed one page Desired qualifications: Good knowledge in programming language theory, algorithms, distributed systems and logic Experience with
-
the mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world
-
Java is a plus. Experience with Linux and basic system administration Working experience with containers, such as Docker (building and management). Experience and interest in Design Patterns, algorithms
-
; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
-
of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied
-
-balance model and hydropower optimization The PhD research fellow will be part of the PhD programme in Computer Science: Software Engineering, Sensor Networks and Engineering Computing (https://www.hvl.no
-
power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
-
; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
-
to improve asset management. However, a key challenge in implementing predictive maintenance is the presence of data uncertainty arising from sensor noise, missing data, fluctuating operating conditions, and