Victor A. Bloomfield – författare

Embarking upon research as a graduate student or postdoc can be exciting and enriching - the start of a rewarding career. But the world of scientific research is also a competitive one, with grants and good jobs increasingly hard to find. "The Chicago Guide to Your Career in Science" is intended to help scientists not just cope with but excel at this critical phase in their careers.Victor A. Bloomfield and Esam E. El-Fakahany, both well-known scientists with extensive experience as teachers, mentors, and adminstrators, have combined their knowledge to create a guidebook that addresses all of the challenges that today's scientists-in-training face. They begin by considering the early stages of a career in science: deciding whether or not to pursue a PhD, choosing advisors and mentors, and learning how to teach effectively. Bloomfield and El-Fakahany then explore the skills essential to conducting and presenting research. "The Chicago Guide to Your Career in Science" offers detailed advice on how to pursue research ethically, manage time, and communicate effectively, especially at academic conferences and with students and peers.Bloomfield and El-Fakahany write in accessible, straightforward language and include a synopsis of key points at the end of each chapter, enabling readers to dip into relevant sections with ease.From students prepping for the GRE to postdocs developing professional networks to managers of corporate labs, scientists at every level will find "The Chicago Guide to Your Career in Science" an unparalleled resource.The toolkit includes advice on: choosing a graduate program; speaking effectively; defending your dissertation; making the most of scientific meetings; writing fellowship and grant proposals; landing a job; pursuing professional contacts; and, choosing an alternative career.

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also:

Explains how to statistically analyze and fit data to linear and nonlinear models Explores numerical differentiation, integration, and optimization Describes how to find eigenvalues and eigenfunctions Discusses interpolation and curve fitting Considers the analysis of time series

Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also:

Explains how to statistically analyze and fit data to linear and nonlinear models Explores numerical differentiation, integration, and optimization Describes how to find eigenvalues and eigenfunctions Discusses interpolation and curve fitting Considers the analysis of time series

Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also: Explains how to statistically analyze and fit data to linear and nonlinear modelsExplores numerical differentiation, integration, and optimizationDescribes how to find eigenvalues and eigenfunctionsDiscusses interpolation and curve fittingConsiders the analysis of time seriesUsing R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.