#PhD position!

I’m looking for a PhD Student (1+3 years) to model insect populations using statistical models + deep learning at #TheoreticalEcology, Regensburg (Germany).

Join our team - please RT!

Details: karriere.uni-regensburg.de/tg7w9

#Insects #Ecology #DeepLearning #Ecology #Monitoring

Moreover, we can use explainable AI tools to understand the learned functional form of the replaced process. We demonstrated this using the Barro Colorado Island plot by replacing the growth process with a DNN. We found plausible dbh-growth and light-growth functions learned by the hybrid model 4/4

We introduce forest-informed neural networks (FINNs), a new DVM in which processes can be replaced by deep neural networks and the entire model is calibrated jointly. FINN can approximate the functional shapes of otherwise misspecified processes and achieve better predictive accuracy 3/4

In our new preprint, “Inferring processes within dynamic forest models using hybrid modeling” @ykaber.bsky.social and I present a new hybrid modeling approach for jointly calibrating a DVM with embedded #deepneuralnetwork arxiv.org/abs/2508.01228 1/4
#deeplearning #forestdynamics

cito can now train DNNs for count data using Poisson or negative binomial distributions. In addition, deep joint species distribution models (#jsdm #sdm) based on the multivariate probit model can be fitted:

An important new feature is hyperparameter tuning under cross-validation, which helps to train the #DNN. Hyperparameter tuning can be easily done by passing a "tune(...)" to the hyperparameters (cito also automatically returns the model with the best hyperparameters):

cito v1.1 #rstats package for deep neural networks (#DL #DNN) (with formula syntax) is now available on #CRAN. New features include likelihoods such as the negative binomial distribution and easy hyperparameter tuning: cran.r-project.org/web/packages...