When quantum computing meets chemistry issue.
Prof Dominique Sugny and Dr Steffen Glaser, OCT specialists from ICB Laboratory, collaborated with Casc4de scientists for a deeper understanding of experimental techniques in analytical sciences
CASC𝟦DE, created in 2014 by Marc-André Delsuc and Bruno Kieffer, is a company specialized in the development of analytical methods along with software and data engineering.
Casc4de develops innovative solutions to tackle the challenges of managing and processing large set of analytical data by combining interdisciplinary skills in the biochemistry, mathematical and data-science fields.
Casc4de carries out its R&D in the spirit of open science (F.A.I.R. data if possible) and open source (free and independent software).
The company also offers its services to answer analytical client’s issues and for multi-analytic approaches, with access to advanced NMR (19F fluorine, 2D, 3D) or mass spectrometry (FTICR, 2D FTMS).
Does the tremendous flow of digits saturate your processes ? Looking for boosted results and real-time treatment ? Available solutions don’t meet to your specific request ?DON’T LET BIG DATA
OVERWHELM YOUR RESEARCH !
CASC4DE builds innovative hardware-enhanced capabilities and softwares to accelerate your research and extract hidden insights from your data flow.
Massive number crunching
Innovative high-speed data processing algorithms and techniques
Packaged solutions for an optimal in-lining into existing processes
Custom Software Development
Embeddable software tools
Nuclear Magnetic Resonance spectroscopy
Remove the Big Data bottleneck
Speed up your results flow
High speed computation within a limited working environment
Technology Solutions created uniquely for Your Needs
Big data and science
As almost all industrial sectors, science is facing some real data challenges.
Instruments, sensors and pictures collect massive amounts of information and raw data, structured or not. Due to the increasing size of data, current technologies require new approaches for an optimized exploitation ! Computation has to be done where the data is and should to be done locally.
A significant amount of data is generated in real time, treatment and handling must be performed « on-the-fly », usually without the possibility of archiving the data flow. Moreover, day-to-day datasets are often disparate, noisy and prone to be corrupted by outliers and artefacts. Despite this complexity, our new algorithms, our new mathematical approaches and our new methodological techniques allow more robust, faster and automatized data-flows.