Services & consulting at the Institute for Applied Simulation
We work out customized solutions to your problems in the areas of statistics, data analysis, simulation and optimization, as well as in issues related to knowledge engineering.
We advise you on issues related to the planning and design of experiments. Our statistics counselling supports ZHAW researchers from all the ZHAW’s Schools, as well as external companies and organizations. We work out answers to your questions on statistics, data analysis, simulation and optimization, and topics related to knowledge engineering.
We implement database applications, and link them to web and smartphone applications. Expert systems, self-learning and bio-inspired algorithms are some of the tools we use to make information quickly accessible and solution-oriented.
We can provide you with training in all aspects of statistics, modelling, simulation and optimization. We have more than 20 years of experience, gathered from over 200 simulation projects, in the area of logistics simulation and optimization.
Our access to computational sciences is based on our knowledge of modelling, simulation, algorithms, programming, and data management.
Our expertise is based on the following methods and techniques, among others:
- time series analysis (uni-/multivariate)
- (recurrent) neural networks
- deep neural networks
- Bayesian networks
- classifiers such as support vector machines and random forests
- clustering algorithms
- dimensionality reduction techniques
- maximum likelihood or Bayesian estimation
- Bayesian prediction
- hypothesis testing and model selection (in either frequentist or Bayesian frameworks)
- optimizing heuristic searches for structures (such as trees, graphs/networks)
- algorithms for computational genomics (such as phylogeny and alignment inference for genomic sequences studies)
- discrete event simulation
- system dynamic modelling
- multi-agent modelling
- multi-physics modelling
- a broad range of programming languages and database technologies
- Hadoop and Spark