"Career Planning for Research Bioscientists" is an essential careers guide for bioscience doctoral students and postdoctoral researchers. It contains a wealth of information and resources specifically targeted at research bioscientists, with practical strategies to enhance career success in an increasingly competitive job market. Advice on how to write a winning CV together with examples adapted for different jobs is presented, as well as practical exercises to assist with skills analysis and decision making. Profiles of PhD-qualified bioscienstists in a range of professions including academic research, industry, science communication, management and consultancy provide valuable insights into how others have managed their careers, and tactics such as networking and using social media demonstrate how new opportunities can be discovered. The content of this book is aimed primarily at research bioscientists, however much of the advice and information will be a useful reference for other students and researchers looking for an effective career planning strategy.

A companion website with additional resources is available at www.wiley.com/go/blackford/careerplanning and you can visit Sarah Blackford's blog at www.biosciencecareers.org for more information.

LAPACK is a library of numerical linear algebra subroutines designed for high performance on workstations, vector computers, and shared memory multiprocessors. Release 3.0 of LAPACK introduces new routines and extends the functionality of existing routines. The most significant new routines and functions include: a faster singular value decomposition computed by divide-and-conquer; faster routines for solving rank-deficient least squares problems: using QR with column pivoting; using the SVD based on divide-and-conquer; new routines for the generalized symmetric eigenproblem: faster routines based on divide-and-conquer; routines based on bisection/inverse iteration, for computing part of the spectrum; faster routine for the symmetric eigen problem using "relatively robust eigenvector algorithm"; new simple and expert drivers for the generalized nonsymmetric eigenproblem, including error bounds; solver for generalized Sylvester equation, used in computational routines Each Users' Guide comes with a "Quick Reference Guide" card.