Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
-
Field
-
., Merilä, P., Vanhatalo, J. & Laine, A.-L. (2024) Inferring ecological selection from multidimensional community trait distributions along environmental gradients. Ecology, 105(9): e4378. Doi: doi.org
-
. Ecology Letters, 28(4): e70003. Doi: 10.1111/ele.70003 Kaarlejärvi, E., Itter, M. S., Tonteri, T., Hamberg, L., Salemaa, M., Merilä, P., Vanhatalo, J. & Laine, A.-L. (2024) Inferring ecological selection
-
diagnosis of psychosis. The postdoctoral researcher will lead a research program focused on developing and testing the computational mechanisms of social inference, although will have plenty of scope, and
-
developing and testing the computational mechanisms of social inference, although will have plenty of scope, and will be encouraged, to develop and expand their own research interests. The postholder will work
-
diagnosis of psychosis. The postdoctoral researcher will lead a research program focused on developing and testing the computational mechanisms of social inference, although will have plenty of scope, and
-
experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
-
techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
-
: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
-
to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
-
models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science