a) Knowledge:

C1 – Knowing and understanding the fundamentals of computer science and mathematics;
C2 – Knowledge of the structure and operation of a computer system;
C3 – Knowledge related to the analysis/design/implementation of IT systems;
C4 – Knowledge about extracting knowledge from data;
C5 – Knowledge of designing/using machine learning analysis methods;
C6 – Knowledge of designing autonomous systems;
C7 – Knowledge of the design and implementation of human-machine interaction;
C8 – The theoretical methodology used in scientific research;
C9 – Carrying out background research, building a hypothesis, testing it, analyzing data and concluding the results;
C10 – Techniques and principles of software development;
C11 – Learning algorithms, coding and testing;
C12 – Testing and Compiling Programming Paradigms in Python;
C13 – Groups of independent step-by-step operations that perform calculations;
C14 – Data processing and automatic reasoning, to solve problems;
C15 – The methods by which the information is generated, structured and stored;
C16 – The methods by which information is maintained, connected, exchanged and used;
C17 – Planning, creating, testing a system;
C18 – Application of the development and management models of the life cycle of a system;

C19 – The process of classifying information into data categories;
C20 – The process of showing relationships between data for some clearly defined purposes;
C21 – Extracting properties from data that is not arranged in a predefined way or does not have a predefined data model;
C22 – Extracting information from data that is difficult to understand and in which patterns are difficult to find;
C23 – Techniques and methods used to obtain and extract information from documents;
C24 – Techniques and methods used for obtaining and extracting properties from unstructured or semi-structured digital sources;
C25 – Methods of artificial intelligence;
C26 – Machine learning methods;
C27 – Statistical methods and databases used to extract content from a data set;
C28 – Techniques for converting unstructured descriptions of a process into a sequence of actions of a finite number of steps;
C29 – Query languages such as SPARQL that are used to retrieve and manipulate data stored in Resource Description Framework (RDF) format;
C30 – The tools, methods and notations used to describe and analyze the characteristics of a business process;
C31 – Business Process Model and Notation (BPMN) and Business Process Execution Language (BPEL);
C32 – Tools, methods and notations used for continuous development;
C33 – Existing techniques and systems used to structure data elements;
C34 – Existing techniques and systems used to demonstrate relationships between data properties;
C35 – Methods of interpreting data structures and the relationships between them;
C36 – Theories, applied principles, architectures and systems of artificial intelligence;
C37 – Intelligent agents;
C38 – Multi-agent systems;
C39 – Specialized systems;
C40 – Rules-based systems;
C41 – Neural networks;
C42 – Ontologies and cognitive theories;
C43 – Techniques and principles of software development;
C44 – Analysis, algorithms, coding, testing and compilation of programming paradigms;
C45 – Object Oriented Programming;
C46 – Functional programming;
C47 – An artificial neural network created for solving artificial intelligence problems;
C48 – Computer systems inspired by biological neural networks;

C49 – Understanding of general computer systems models and its elements;
C50 – Knowledge of the possibilities of use for automation purposes;
C51 – The type of infrastructure that defines the data format: semi-structured, unstructured and structured;
C52 – Visual representation and interaction techniques;
C53 – Histograms, scatterplots, surface plots, treemaps and parallel coordinate plots;
C54 – Presentation of abstract numerical and non-numerical data to enhance people’s understanding of this information;
C55 – Understanding the fundamental concepts of artificial intelligence;
C56 – Knowledge of different machine learning algorithms;
C57 – Knowledge of image segmentation and classification methods;
C58 – Knowledge of the basic principles of robotics and autonomy;
C59 – Knowledge of the principles of ethics and responsibility in the field of artificial intelligence;
C60 – Knowledge of machine learning techniques applied in agriculture and machine learning for crop optimization;
C61 – Knowledge of machine learning techniques applied in the field of finance and economic forecasts;
C62 – Knowledge of machine learning techniques applied in the field of cyber security;
C63 – Knowledge of machine learning techniques applied in the field of artificial vision;
C64 – Knowledge of dimensionality reduction and feature selection techniques;
C65 – Knowledge of the theory and principles behind neural networks;
C66 – Experience in data analysis and interpretation;
C67 – Experience in the application of natural language processing techniques;
C68 – Experience in developing machine learning algorithms for social media data analysis;
C69 – Experience in developing machine learning models for gesture and movement recognition;
C70 – Experience in the development of voice assistance systems and chatbots;
C71 – Experience in working with large volumes of data and using data preprocessing techniques;
C72 – Experience using machine learning algorithms to optimize and personalize recommendations.

b) Skills:

A1 – Ability to use algorithms and machine learning techniques to solve complex problems;
A2 – Ability to work with large volumes of data and process them in order to apply machine learning algorithms;
A3 – Ability to implement neural network models and train them with relevant data sets;
A4 – Ability to use programming languages such as python, R or matlab in the development and implementation of machine learning algorithms;
A5 – Ability to apply data preprocessing techniques to ensure their quality and relevance in the machine learning process;
A6 – The ability to analyze and interpret the results obtained through machine learning algorithms;
A7 – Ability to apply machine learning algorithms for natural language recognition and generation;
A8 – Ability to work with convolutional neural networks for image processing and object recognition;
A9 – Ability to use machine learning algorithms to optimize and personalize recommendations in the field of e-commerce;
A10 – The ability to apply machine learning techniques in the field of medicine for the diagnosis and prognosis of diseases;
A11 – Ability to use machine learning algorithms to analyze and classify texts;
A12 – Ability to work with machine learning models for the detection and prevention of fraud in the financial field;
A13 – Ability to use machine learning techniques to analyze and interpret social media data;
A14 – The ability to work in multidisciplinary teams for the development and implementation of solutions based on artificial intelligence;
A15 – Ability to effectively communicate results and findings in the field of artificial intelligence through presentations and reports;
A16 – The ability to have an ethical and responsible approach in the use of artificial intelligence;
A17 – Ability to use machine learning techniques in the field of computer vision to recognize objects, features and movements;
A18 – Ability to apply machine learning algorithms to generate creative content, such as texts, images or music;

A19 – Ability to use machine learning techniques to analyze and predict financial and market data;
A20 – Ability to work with autonomous robots and intelligent control systems based on artificial intelligence;
A21 – Ability to apply machine learning algorithms in the field of autonomous vehicles and driving assistance systems;
A22 – Ability to use machine learning techniques in agriculture to optimize production and monitor crops;
A23 – Ability to work with machine learning algorithms to analyze and predict climate patterns and climate change;
A24 – Ability to use machine learning techniques in cyber security to detect and prevent threats;
A25 – Ability to apply machine learning algorithms for the development of virtual assistants and intelligent chatbots;
A26 – Ability to use machine learning techniques in the field of machine translation and natural language understanding;
A27 – Ability to work with machine learning algorithms to analyze and predict user behavior in the field of marketing;
A28 – Ability to use machine learning techniques to develop interactive games and simulations;
A29 – The ability to apply machine learning algorithms in the field of medicine to identify new drugs and personalized therapies;
A30 – Ability to work with machine learning techniques in the field of natural language processing for automatic translation and text summarization;
A31 – Ability to use machine learning techniques for data analysis in health and personalized medicine;
A32 – Ability to work with machine learning algorithms to detect and prevent fraud in the banking and financial system;
A33 – The ability to apply machine learning techniques for the analysis and classification of medical images in the diagnosis of diseases;
A34 – The ability to use machine learning algorithms to optimize the route and scheduling of public transport;
A35 – Ability to work with machine learning techniques to analyze and predict financial markets and exchange rates;
A36 – Ability to use machine learning algorithms in the field of voice recognition and voice assistants;
A37 – Ability to apply machine learning techniques to analyze and classify sentiments from social texts and reviews;
A38 – Ability to use machine learning algorithms in the field of collaborative robots and human-machine interaction;

A39 – Ability to work with machine learning techniques to optimize energy efficiency in buildings and industrial systems;
A40 – Ability to use machine learning algorithms to analyze and predict user behavior in recommender systems;
A41 – The ability to apply deep learning methods for the recognition and analysis of complex signals, such as eeg (electroencephalography) or ecg (electrocardiography) signals;
A42 – Ability to use machine learning algorithms for creating artificial speech models and natural language generation.

c) Responsibility and autonomy:

R1 – The ability to solve specific tasks autonomously;
R2 – The ability to identify innovative solutions and ideas;
R3 – The ability to effectively plan specific tasks;
R4 – The ability to effectively manage resources;
R5 – The ability to assume tasks, compliance with ethical principles;
R6 – The ability to adapt to new requirements;
R7 – The ability to apply machine learning algorithms to solve practical problems;
R8 – The ability to apply machine learning techniques in the medical field for diagnosis and prognosis;
R9 – Ability to develop machine learning models for natural language recognition and generation;
R10 – Ability to develop deep learning models for object recognition in images;
R11 – The ability to evaluate and compare the performance of different machine learning models;
R12 – The ability to implement machine learning algorithms on mobile devices or in the embedded environment.