Computer vision engineers research, design, develop, and train artificial intelligence algorithms and machine learning primitives that understand the content of digital images based on a large amount of data. They apply this understanding to solve different real-world problems such as security, autonomous driving, robotic manufacturing, digital image classification, medical image processing and diagnosis, etc.
No competences in this bucket.
Model designed to generate a virtual representation of an object or system updated from real-time data. The virtual representation process is through the combination of data and technology simulation, using sensors to produce data of the physical object, such as temperature or energy to build its digital twin. Machine learning, simulation and reasoning are involved in this process.
The suite of software development tools for writing programs, such as compiler, debugger, code editor, code highlights, packaged in a unified user interface, such as Visual Studio or Eclipse.
The artificial intelligence theories, applied principles, architectures and systems, such as intelligent agents, multi-agent systems, expert systems, rule-based systems, neural networks, ontologies and cognition theories.
The techniques and principles of software development, such as analysis, algorithms, coding, testing and compiling of programming paradigms in Python.
The techniques and principles of software development, such as analysis, algorithms, coding, testing and compiling of programming paradigms (e.g. object oriented programming, functional programming) and of programming languages.
A programme run on a computer that represents dynamic responses of a system to explore a mathematical model behaviour, using a model of a real system, composed of mathematical equations.
The process of developing and constructing systems for implementing data collection and analysis at large scale.
The field of study that deals with big amount of data using AI techniques such as machine learning algorithms to predict patterns and obtain useful information to make business decisions
The different aspects and practices of image processing and manipulation like image interpolation, aliasing, image enhancement, contrast stretching, histogram processing and equalization, singular value decomposition, singular value equalization, wavelets filtering and many others.
A process designed to detect and identify a feature or object in an image or video. This process is used in medical imaging, security surveillance or defect detection, among other fields. Key technique for a wide range of applications such as automated driving, image classification, or visual inspection.
The principles, methods and algorithms of machine learning, a subfield of artificial intelligence. Common machine learning models such as supervised or unsupervised models, semi- supervised models and reinforcement learning models.
The interdisciplinary field between computer science, mathematics and engineering. It concerns the employment of technical approaches and theoretical frameworks, and the use of computers, to address issues in science and engineering.
The study of statistical theory, methods and practices such as collection, organisation, analysis, interpretation and presentation of data. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments in order to forecast and plan work-related activities.
Create a customised software for processing data by selecting and using the appropriate computer programming language in order for an ICT system to produce demanded output based on expected input.
Create a first incomplete or preliminary version of a piece of software application to simulate some specific aspects of the final product.
Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
Develop and manage methods and strategies used to maximise data quality and statistical efficiency in the collection of data, in order to ensure the gathered data are optimised for further processing.
Reduce data to their accurate core form (normal forms) in order to achieve such results as minimisation of dependency, elimination of redundancy, increase of consistency.
Utilise collections of codes and software packages which capture frequently used routines to help programmers simplify their work.
Use software tools (CASE) to support the development lifecycle, design and implementation of software and applications of high-quality that can be easily maintained.
Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.
Conduct a comprehensive and systematic research of information and publications on a specific literature topic. Present a comparative evaluative literature summary.
Specify technical properties of goods, materials, methods, processes, services, systems, software and functionalities by identifying and responding to the particular needs that are to be satisfied according to customer requirements.
Create visual representations of data such as charts or diagrams for easier understanding.
Apply and combine different computer vision tools and methods such as image acquisition, image processing, image segmentation and classification, detection, etc. in one system to allow computers to extract information from digital images such as photographs or video.
Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
Collect and select a set of data from a population by a statistical or other defined procedure.
Apply quality analysis, validation and verification techniques on data to check data quality integrity.
Analyse data gathered from sources such as market data, scientific papers, customer requirements and questionnaires which are current and up-to-date in order to assess development and innovation in areas of expertise.
Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.
Reduce the number of variables or features for a dataset in machine learning algorithms through methods such as principal component analysis, matrix factorization, autoencoder methods, and others.
Produce research documents or give presentations to report the results of a conducted research and analysis project, indicating the analysis procedures and methods which led to the results, as well as potential interpretations of the results.
No competences in this bucket.
The interdisciplinary field between cognitive science and computer science that involves simulating human thinking processes through a computerised approach. It makes use of algorithms for data mining and natural language processing to imitate the functioning of the human brain.
The branch of computer science that follows the principles of quantum theory. It uses subatomic particles which are allowed to exist under more than one state thanks to quantum bits, or qubits.
The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.
The query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).
The algorithms, applications and implementations that deal with the processing and transferring of information through analog or digital frequencies.
The field that merges approaches from computer science and design. It involves the process of creating images, 3D models generation, as well as the rendering, texturing and lighting them. Its applications vary from digital photography, film and video games, and the display of images on electronic devices.
The principles, methods and algorithms of deep learning, a subfield of artificial intelligence and machine learning. Common neural networks like perceptrons, feed-forward, backpropagation, and convolutional and recurrent neural networks.
Systems to store, process and spread information in digital form. They are hardware, software and networks, as well as the ways in which these can be employed.
The principles and factors determining the formation of an image such as geometry, radiometry, photometry, sampling and analog to digital conversion.
The process of establishing a mathematical representation problem, such as equations, of a real-word to provide insights, discover new features about the problematic scenario, better understand the original problem or to make predictions about it.
The process of fusing input data measurements and a mathematical model to determine the internal state of an energy system
No competences in this bucket.
Use specific techniques and methodologies to analyse the data requirements of an organisation's business processes in order to create models for these data, such as conceptual, logical and physical models. These models have a specific structure and format.
Repair computer code by analysing testing results, locating the defects causing the software to output an incorrect or unexpected result and remove these faults.
Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.
Create software or device components which enable interaction between humans and systems or machines, using appropriate techniques, languages and tools so as to streamline interaction while using the system or machine.
Explore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.
Utilise computer languages that are syntactically distinguishable from the text, to add annotations to a document, specify layout and process types of documents such as HTML.
Gather relevant information by applying systematic methods, such as interviews, focus groups, text analysis, observations and case studies.
Execute a systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques.
Plan scholarly research by formulating the research question and conducting empirical or literature research in order to investigate the truth of the research question.