RC1: Being able to assess and compare the results obtained.
RC2: Being able to apply the knowledge acquired and resolve problems in new, multidisciplinary environments, often in a research context.
RC3: Understanding and being able to use scientific Information and Communication Technology.
RC4: Understanding and being able to use basic research tools in the fields of IAI and OAI.
RC5: Being able to communicate one's work clearly and unambiguously to both specialist and non-specialist audiences.
RCO10: Learning about efficient algorithms for solving real scientific problems associated with big data processing.
RCO11: Learning about and how to study deep neural networks for use in industrial systems.
RCO12: Learning the basics of finite element calculation.
RCO13: Learning about and how to use CFD numerical simulations and heat transfer analyses to optimise the design of components and systems.
RCO14: Learning how to model and simulate production systems and processes.
RCO15: Learning about time series, anomalies and the specifics of applying artificial intelligence techniques in a context of ongoing data generation.
RCO16: Learning how to locate and interpret relevant, up-to-date information in databases, repositories, publishers, etc.
RCO17: Learning about the ethical aspects of engineering in general and particularly in IAI and OAI, and understanding one's duty to protect industrial and intellectual property in professional practice.
RCO18: Learning about the theories, key concepts, parameters and computer tools used to simulate manufacturing processes.
RCO1: Learning about the basic principles and most commonly-used techniques, and learning to select the most appropriate machine learning technique in accordance with the problem and the nature of the data, in order to be able to apply it in a real Artificial Intelligence environment in industrial applications.
RCO2: Learning about the kinematics and dynamics of mechanical systems.
RCO3: Understanding the techniques of reinforcement learning and learning how to apply it and how to recognise situations in which it can be applied.
RCO4: Learning how to acquire data from different types of analogue and digital sensors.
RCO5: Understanding the concepts, methods and architectures most commonly used in big data.
RCO6: Understanding the principles of security, risk analysis, cryptographic techniques and privacy assurance in computer systems and operating systems.
RCO7: Learning about penetration testing and ethical hacking techniques.
RCO8: Learning about and being able to identify and use the hardware required for an IAI application.
RCO9: Learning about the principle theories associated with the physical parameters of image generation and associated advanced concepts in projects linked to this field.
RHE10: Being able to determine which reinforcement learning techniques are most suitable in each case.
RHE11: Being able to define and implement a solution based on reinforcement learning.
RHE12: Being able to transfer the values read by sensors to remote devices.
RHE13: Being able to apply storage, processing and analysis techniques to large volumes of data.
RHE14: Being able to generate visualisations based on information extracted from big data.
RHE15: Being able to apply and assess methods of accessing systems based on physical and biometric information cues.
RHE16: Being able to identify ethical dilemmas and be aware of the biases of machine learning when applied to humans.
RHE17: Being able to understand, use and assess safety management procedures in industrial systems.
RHE18: Being able to identify different cyberattack scenarios: evidence collection and forensic analysis.
RHE19: Being able to configure the hardware required for an IAI application.
RHE1: Being able to develop smart control techniques in industrial applications.
RHE20: Being able to select the hardware required for an IAI application.
RHE21: Being able to use advanced image, video and signal processing tools within the field of machine vision.
RHE22: Being able to analyse scientific problems that can be resolved computationally.
RHE23: Being able to use Python algorithms, selecting the most appropriate programming environments and libraries in each case.
RHE24: Being able to generate graphic representations of data in Python that enable a better understanding and visualisation of different phenomena.
RHE25: Being able to develop applications for deep artificial neural networks.
RHE26: Being able to assess and deploy deep neural networks.
RHE27: Being able to study a deep neural network in order to run it in a real time industrial system.
RHE28: Being able to develop industrial applications such as classification, non-linear regressions, object detection and development of control algorithms based on these techniques.
RHE29: Being able to apply finite element calculations to industrial components, using commercial software.
RHE2: Being able to distinguish between a supervised and an unsupervised learning problem.
RHE30: Being able to apply the concepts of the finite volume method to fluid mechanics and heat transmission problems.
RHE31: Being able to use different Artificial Intelligence techniques to detect anomalous situations, in accordance with the nature of the data and the anomaly.
RHE32: Being able to use bibliographic managers as a tool for organising bibliographic information. Being able to compile state-of-the-art reports on subjects linked to IAI and OAI.
RHE33: Being able to draft proposals for research projects.
RHE34: Being able to present research results orally and in writing.
RHE35: Being able to develop design and manufacturing functions for different productive sectors.
RHE36: Being able to draft technical and diagnostic reports on industrial component manufacturing processes.
RHE37: Being able to verify and monitor the quality of manufactured products.
RHE38: Being able to use simulation programs based on the finite element technique to design tools that will enable design optimisation.
RHE39: Being able to analyse, design, simulate and optimise processes and products.
RHE3: Being able to identify and recognise the basic elements of a smart control system.
RHE4: Being able to design a smart control system and analyse its stability.
RHE5: Being able to use the software tools most commonly used in industrial machine learning applications.
RHE6: Being able to understand and apply the above skills to mechanical systems in different industries, such as health, energy, robotics, etc.
RHE7: Being able to identify vibration problems in different mechanical systems.
RHE8: Being able to identify problems in which it would be feasible to use reinforcement learning techniques.
RHE9: Being able to formalise a problem in terms of the Markov Decision Process.
RHT1: AUTONOMY AND SELF-REGULATION: managing one's time and effort autonomously and in a self-regulated way in order to achieve goals and targets.
RHT2: COMMUNICATION AND MULTILINGUALISM: understanding concepts and ideas and expressing them clearly both orally and in writing, taking into account the structures and norms of specialist written communication when preparing academic and/or scientific documents.
RHT3: INNOVATION AND ENTREPRENEURSHIP: proposing creative and innovative solutions to a situation or problem.
RHT4: TEAMWORK: actively collaborating to achieve common goals, exchanging information, shouldering responsibilities, assuming leadership roles, resolving problems and contributing to the entire group's improvement and development.
