Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
is internationally recognized, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference
-
a vacancy for a PhD position at the Department of Information and Media Studies. The position is for a fixed-term period of 4 years, of which 25% will be dedicated to teaching, supervision and
-
academic writing (e.g., previous conference or journal articles) Personal qualities Be highly motivated for completing a PhD Be open-minded and eager to learn Be goal-oriented, accurate, analytical and
-
into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
-
complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
-
into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
-
. You will become part of a dynamic, collaborative working environment with expertise in drilling engineering, geomechanics, machine learning, and energy systems. The project will integrate real‑time
-
herbivores space use behavior in relation to snow conditions, data is required at cm to m resolution. This PhD project will develop and apply remote sensing methods to advance terrestrial snow monitoring
-
. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By
-
optimization (WOB, RPM, flow rate, etc.) using machine learning techniques Anomaly detection for downhole vibrations, bit failure, and circulation losses Integrating physical modeling, digital twins, and data