Modelling and simulation
- Mechanical or structural.
- Terms o Thermodynamics.
- Manufacturing processes: injection moulding, stamping, forging, additive manufacturing, etc.
- Electronic and/or electromagnetic.
- Fluids: gases, liquids.
- Fluid-structure interaction.
- Acoustic o Vibroacoustics.
- Chemical Kinetics.
- Multiphysics phenomena.
- Valuation of financial products and their risks, portfolio management.
Statistics and Big Data
- Statistical advice and data analysis.
- Time series prediction.
- Mapping from spatial data.
- Environment and energy statistics.
- Tourism statistics.
- Customer, market and product research.
- Design of experiments, clinical trials.
- Biostatistics, epidemiology.
- Health applications.
- Quality control and reliability.
- Production, process and stock control.
- Statistical applications for industry or public administrations.
- Process optimization.
- Stock optimization.
- Resource optimization.
- Transport route planning.
- Support for optimal decision making.
- Automatic learning.
- Neural networks.
- Bayesian networks.
- Deep learning.
- Natural language processing.
- Image processing.
- Multimedia signal processing.
- Machine learning.
- Development of digital twins.
- Smart access networks.
- Smart services (health, education, government,…).
- Programming in scientific languages: Fortran, C, C++, Pyton, Matlab, R.
- Development of software packages.
- Development of graphical interfaces.
- Implementation of commercial and free software packages.
- Parallelisation of algorithms.
- Use of commercial software packages.
- Use of open source packages.
- Programming on GPUs.
- Distributed computing.
- High performance computing.
- Hardware configurable.
- WEB applications / technologies.
- Data modelling / databases.
- Quantum computing.
- Computación en la nube/de niebla/de borde.
- Quantum cryptography.
Areas of experience:
Challenge 1: Health
- Visualisation, processing and analysis of medical images. Characterisation of false positives. Learning models using large databases (machine/deep learning).
- Characterisation of habits in the population. Simulation of the flow of users by services.
- Optimising the planning of the location of primary health care facilities in environments uncertain of the evolution of demographic rates over a time horizon.
- Optimisation of health procurement planning in environments uncertain of the evolution of demographic rates and types and intensity of diseases and epidemics over a time horizon.
- Optimisation of ambulance fleet types: Sizing and location in an uncertain service demand environment.
- Optimising the distribution of health resources, both human and material, in emergency situations.
Challenge 3: Energy
- Optimisation of electricity generation in industrial and residential buildings for self-consumption and its connection to the distribution grid.
- Optimisation of energy distribution networks.
- Numerical simulation of heat transfer and combustion processes.
- Numerical simulation of thermoelectric, thermomagnetic and thermomechanical processes.
- Decision support in energy processes.
Challenge 5: Environment
- Simulation, prediction and control of pollutant emissions.
- Simulation, optimisation and control of production and distribution processes.
- Integral optimisation of product and goods supply chains.
- Development of systems to help prevent, control and extinguish fires.
- Optimising the planning of emergency resource use.
- Optimising emergency action.
- Simulation, prediction and impact of natural disasters such as floods and earthquakes.
- Simulation and prediction of water quality.
- Optimisation of irrigation planning for agricultural and livestock purposes.
- High-dimensional phenotypic data.
- Cryospheric sciences.
- Traceability of waste and raw materials.
- Remote sensing systems for resource optimisation.
- AI applied to the optimisation of natural resources.