Experiment with algorithmic design models (e.g., for balancing aesthetic, performance, and design feasibility) in different case studies. Explore and test the automatic extraction of technical documentation from architectural models. Experiment with rationalization strategies for architectural design problems.

The goal is to develop new models and algorithms for multi-objective combinatorial optimization to be applied in the domain of software engineering.

The Carnegie Mellon Portugal Program (CMU Portugal) through Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID) – as host institution “Entidade de Enquadramento” - and with the support of Fundação para a Ciência e Tecnologia (FCT) is announcing a Call for Expressions of Interest under the Visiting Students Program, for visits to Carnegie Mellon University in 2022. This Program is one of the key educational initiatives of the CMU Portugal partnership, under which successful applicants pursuing a master’s degree and affiliated with a Portuguese Higher Education Institution, or who have finished their master’s degree in Portugal in the area of Information and Communication Technologies (ICT) in the past two years, can spend two(2) to five(5) months working in ICT research and immersed in the Carnegie Mellon’s culture, while being mentored by CMU leading faculty and researchers.

IntelligentCare project develops a multimorbidity management decision support system focused on the patient's vision of value, i.e., achieving the outcomes that matter for patients. To this end, analytical methodologies to identify high risk multimorbidity patient groups, as well as to evaluate and define evidence-based clinical pathways associated with each patient group will be used. The project contributes to improved healthcare delivery through precision and personalized medicine and to contribute to market sustainability using new technologies. INESC-ID leads a project activity for identifying and and characterizing the main clusters of multimorbidity in clinical data. The applicant to be hired will join a team engaged in performing exploratory analysis of clinical data for clinical phenotyping and identification of most frequent representative groups iin multimorbidity, and investigation and exploration of different data mining algorithms supporting this task.

Develop tools to automate testing of interactive system, in particular in Augmented Reality (AR) interfaces. The work involves using the iv4XR AR testing framework to create a used case that shows the applicability of the iv4XR testing agents to AR. A simple AR game will be developed for the purpose, based on the current demo. The configuration of the testing agents may involve the creation of additional tools to improve the demonstrative impact of the test case for the iv4XR project.

The candidate will work on Tasks 2-3 of the project, addressing the problem of model-based reinforcement learning in non-stationary environments using deep learned models. The key objectives are: 1. Conduct an extensive survey of state-of-the-art on model-based reinforcement learning in non-stationary environments, where non stationarity occurs from the presence of other agents in the environment, and planning using the learned models. 2. Develop new methods for learning disentangled latent representations for such processes that can be used to enable the emergence of communication. 3. Extend MCTS planning algorithms to handle the models learned in 2.

At the meeting held at April 26 of 2022 the Executive Board of INESC-ID – Instituto de Engenharia de Sistemas e Computadores INESC-ID made the decision to open an international selection call for one doctorate position in the scope of the project EV4EU | 101056765, in the form of an employment contract under an uncertain term work contract – in the framework of abovementioned legislation being the basis of the contracting the performance of a specific service, precisely defined and non-durable, with a view to performing the following functions: · Manage the activities that should be developed during the meetings · Assure the respect of the deadlines in the project · Organise and coordinate meetings (Internal meetings, consortium meetings, Meetings with comission, etc) · Colaborate in definition of communication and dessimination activities · Collaborate in the development of new proposals The expiration of the contract that will operate with the communication referred to in article 345 (1) of the Código do Trabalho (Labor Code), meaning that the employer shall notify the termination of the contract to the employee, at least 7, 30 or 60 days in advance, according to whether the contract lasted up to six months, six months to two years, or per longer period.

At the meeting held at April 26 of 2022 the Executive Board of INESC-ID – Instituto de Engenharia de Sistemas e Computadores INESC-ID made the decision to open an international selection call for one doctorate position in the scope of the project EV4EU | 101056765, in the form of an employment contract under an uncertain term work contract – in the framework of abovementioned legislation being the basis of the contracting the performance of a specific service, precisely defined and non-durable, with a view to performing the following functions: Develop methodologies promoting the massive development of electric vehicles Supervision of PhD students Coordinate the scientific tasks with other partners Scientific publications The expiration of the contract that will operate with the communication referred to in article 345 (1) of the Código do Trabalho (Labor Code), meaning that the employer shall notify the termination of the contract to the employee, at least 7, 30 or 60 days in advance, according to whether the contract lasted up to six months, six months to two years, or per longer period.

Chit-chat dialogue systems are currently trained in an end-to-end fashion with large collections of text corpora, resulting in pre-trained models that can be fine-tuned to various dialogue tasks. However, and as stated by several authors, one of the bottlenecks of these systems is that they do not display a consistent profile/persona/personality, a characteristic that is essential in task-oriented dialogues, such as in customer support settings, where, for instance, the (in)formality of the conversation/bot should be constant. We believe that to improve the current state-of-the-art of chit-chat dialogue systems, more specific annotated datasets are needed. Nevertheless, there is a lack of such datasets. Exceptions are, for instance, the Persona-Chat dataset and subtitles datasets such as the Cornell Movie-Dialogs Corpus and the Friends dataset, that include basic profile information about the speakers. Nevertheless, all these corpora only exist for English, and are limited in the number of profiles/personas involved. Although not usually abundantly annotated, movie/series scripts contain information that could help improve chit-chat models: in movie scripts, each character line identifies the speaker, however the persona of the speaker is usually known. On the other hand, most subtitles datasets do not have this information, but exist in large quantities, for many languages, and are publicly available. In this work, the candidate will explore how to extract and transfer profile/persona traits from movie/series scripts/subtitles datasets with the purpose of improving chit-chat dialogue systems. We will take advantage of deep learning models, but we will also resort to rule-base systems if needed. Moreover, we will take advantage of recent studies using latent action representations (VAEs, GANs, etc.) to capture persona features and speaker’s characteristics, and thus transfer this learned knowledge to other dialogue tasks.