Computational science

In some cases, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms.

Trying to predict, understand and somehow shape the development of cities in the future requires complex thinking and computational models and simulations to help mitigate challenges and possible disasters.

Their behavior is of unprecedented complexity and the characterization and measurement of the risk inherent to this highly diverse set of instruments is typically based on complicated mathematical and computational models.

Solving these models exactly in closed form, even at a single instrument level, is typically not possible, and therefore we have to look for efficient numerical algorithms.

Examples of these techniques are high-throughput sequencing, high-throughput quantitative PCR, intra-cellular imaging, in-situ hybridization of gene expression, three-dimensional imaging techniques like Light Sheet Fluorescence Microscopy, and Optical Projection (micro)-Computer Tomography.

Given the massive amounts of complicated data that is generated by these techniques, their meaningful interpretation, and even their storage, form major challenges calling for new approaches.

Going beyond current bioinformatics approaches, computational biology needs to develop new methods to discover meaningful patterns in these large data sets.

A major challenge here is to understand how gene regulation is controlling fundamental biological processes like biomineralization and embryogenesis.

Commonly applied methods include: Historically and today, Fortran remains popular for most applications of scientific computing.

[32][33] Other programming languages and computer algebra systems commonly used for the more mathematical aspects of scientific computing applications include GNU Octave, Haskell,[32] Julia,[32] Maple,[33] Mathematica,[34][35][36][37][38] MATLAB,[39][40][41] Python (with third-party SciPy library[42][43][44]), Perl (with third-party PDL library),[citation needed] R,[45] Scilab,[46][47] and TK Solver.

Computational science application programs often model real-world changing conditions, such as weather, airflow around a plane, automobile body distortions in a crash, the motion of stars in a galaxy, an explosive device, etc.

For example, in weather models, each item might be a square kilometer; with land elevation, current wind direction, humidity, temperature, pressure, etc.

In this program, students: ETH Zurich offers a bachelor's and master's degree in Computational Science and Engineering.

George Mason University has offered a multidisciplinary doctorate Ph.D. program in Computational Sciences and Informatics starting from 1992.