NEWS
Tags: 

RTS Life Science appoints Mid-Atlantic sales manager

RTS Life Science, a worldwide supplier of automated sample management and drug delivery testing systems, has appointed Richard Carpenter as the Mid-Atlantic sales manager. Carpenter has extensive experience in the scientific instrument market, and that his appointment will expand the US field-based sales team.

Prior to joining RTS, Carpenter worked for LabCyte and Tecan where he became an expert in robotics and liquid handling systems. Commenting on his appointment, Carpenter said: 'RTS is an exciting company to work for, with an increasingly extensive product range. With over 20 years' experience in the drug discovery arena and an impressive installed base, I look forward to introducing the product range further into the pharma, biotech and academic institutes of the East Coast.'

Twitter icon
Google icon
Del.icio.us icon
Digg icon
LinkedIn icon
Reddit icon
e-mail icon
Feature

Building a Smart Laboratory 2018 highlights the importance of adopting smart laboratory technology, as well as pointing out the challenges and pitfalls of the process

Feature

Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory

Feature

This chapter will consider the different classes of instruments and computerised instrument systems to be found in laboratories and the role they play in computerised experiments and sample processing – and the steady progress towards all-electronic laboratories.

Feature

This chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined-up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value

Feature

This chapter takes the theme of knowledge management beyond document handling into the analysis and mining of data. Technology by itself is not enough – laboratory staff need to understand the output from the data analysis tools – and so data analytics must be considered holistically, starting with the design of the experiment