Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
Job Description Do you want to figure out why Bayesian deep learning doesn’t work? And afterwards fix it? At DTU Compute we are working towards building highly scalable Bayesian approximations
-
for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
-
sciences Strong background in deep learning, with experience in probabilistic models (e.g., Variational Autoencoders, Bayesian approaches) Proficient Python programming for machine learning and scientific
-
postdoctoral researcher is expected to make significant contributions to the project’s research objectives, demonstrating both technical excellence and collaborative ability. Key performance expectations include
-
-facing messaging aligned with EQA’s objectives. Cross-team coordination and operational leadership Acting as a bridge between scientific content, project operations, and consortium dynamics. Supporting
-
current focus areas cover large-scale structure of the universe, physics of compact objects, exoplanets, upper atmosphere physics and cosmo-climatology as well as development of instrumentation, in
-
technologies. Analyze and evaluate WGS data to support research and capacity-building objectives, including participation in external quality assessments and genomic proficiency tests. Providing bioinformatics
-
and/or nanofabrication being a plus. Who we are The Department of Physics and Astronomy is a department on Natural Sciences. The main objectives of the Department are to carry out research
-
particular, the candidate will investigate enhancement of RL controllers in multi-agent settings and by incorporating optimization techniques such as multi-objective optimization. Overall, the candidate will
-
entities, such as robots, vehicles, or sensors, forms internal representations of space, time, and motion when interacting in complex non-stationary environments. The objective is to study and develop models