Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
hydrological model simulations. The goal is to develop a novel synergy between(sequential) calibration/data assimilation and machine learning/deep learning methods to better understand hydro-meteorological
-
environmental and heritable factors in a family tree of over 6 million Danes affect disease risk, to using machine learning on genetic and phenotypic data to define clusters of patients with different disease
-
developments in machine learning and finite temperature modelling to design materials for energy applications, such as solid-state electrolytes for batteries. The prospective PhD student should have an interest
-
In line with the commitment to building a theoretical foundation of safe reinforcement learning, we are looking for a highly motivated and talented PhD student to join our team. The position is a
-
areas(i) density functional theory calculations,(ii) molecular dynamics simulations,(iii) machine learning applied to chemistry or materials science Experience with scientific programming(e.g. in python
-
Postdoc in statistics to develop Bayesian privacy metrics for synthetic health data (2024-224-05725)
one or more of these additional criteria: Synthetic data generation Machine learning Large health register data GDPR compliance rules Python Linux Valued personal competencies are: Independent and
-
of AIS data to trajectories for further analyses. The open position is targeted towards ensuring the validity of the AIS data at multiple level. For this task, we envision developing machine-learning
-
requires internationally recognized research experience within one or more of the following areas: communication networks control systems sound machine learning signal processing robotics antennas The main