Sort by
Refine Your Search
-
-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
-
or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
-
four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. More about the position / Project description The research
-
to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
-
their first years) PhD Fellows may acquire pedagogical competency upon agreement How to apply The application with attachments must be submitted in our electronic recruiting system. Please follow the link
-
The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
-
(international staff may also benefit from tax cuts in their first years) PhD Fellows may acquire pedagogical competency upon agreement How to apply The application with attachments must be submitted in our
-
understanding of adaptive immune receptor (antibody and T-cell receptor) specificity using high-throughput experimental and computational immunology combined with machine learning. The long-term aim is to
-
degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system is required. The candidate must have interest and solid background in software systems, machine learning
-
economics of ICTs / AI. Experience with one or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data