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Looking for PhD Scholarship / Vacancy | MSc Mechanical Engineering | Innovating with AI & Numerical Optimization
I have an MSc in Mechanical Engineering and am aiming to pursue a PhD. I am currently working as a Research Assistant at the Technical University of Darmstadt. My passion lies in developing data science, AI, machine learning, and numerical optimization skills, particularly in the context of mechanical engineering. With a strong foundation in Mechanical Engineering and Data Science, I am well-equipped to tackle challenging projects and contribute to scientific advancements in the field.
Throughout my career, I have been dedicated to helping the industry and individuals grow by leveraging my expertise. I have had the privilege of working as a research associate at the Technical University of Munich, where I gained valuable experience in various aspects of engineering and data science.
Today, I am fostering external collaboration for applied research with people, institutions, and companies in order to take advantage of diversity and inclusion. I have spent hundreds of hours giving private tutoring sessions to students from highly-ranked universities, and have reviewed dozens of scientific papers. I have experience in project management related to research.
Please feel free to reach out to me!
E-mail: [email protected] / [email protected]
https://scholar.google.com/citations?user=r70Opz0AAAAJ&hl=pt-PT&oi=ao
Technische Universität Darmstadt · Full-time
🚀 Supervised 8 MSc/BSc theses 🚀 Collaborated with industry partners on applied research projects 🚀 Conducted multi-objective optimization of corrugated board structures 🚀 Developed graph-based methods for analyzing corrugated boards 🚀 Authored 2 competitive research proposals 🚀 Published 10+ academic papers, including 3 in Q1 journals and 1 in a leading field-specific journal
Skibby Software Limited · Contract
As a consultant for Skibby, I specialize in AI-related software development aimed at assisting engineers and stakeholders in analyzing extensive documentation. 👩💻 My role involves leveraging advanced technology to enhance the efficiency and accuracy of data processing, enabling a streamlined and effective approach to document analysis.🚀 By harnessing the power of artificial intelligence, I contribute to Skibby's mission of providing an enterprise-ready back-office automation solution tailored specifically for construction companies.🏗️ Skills: Python (Programming Language)
Technical University of Munich · Full-time
✔️ Simulation of industrial components using Simulink; ✔️ Data Mining manufacturing use case using Python; ✔️ Responsible for a project of MATLAB / Simulink simulation of a Robot-like system; ✔️ Responsible for the Data Mining-related lectures. Skills: Data Mining
🚀 Designing mechanical components; 🚀 Solving mechanical engineering-related issues concerning the project; 🚀 Simulations and lab experiments. Skills: Mechanical Engineering · Teamwork · SOLIDWORKS IPR_pres
Universidade do Porto · Contract
As a monitor of the Junior University program, I am responsible for a hands-on activity for two weeks for High-School students related to IoT Skills: Lecturing · Logistics Management · Education · Communication · Internet of Things (IoT) UJ3 UJ2 UJ1
Porto Space Team aims to be a group of development and research on topics related to Space and to be an opportunity for collaboration. Responsibilities: to create conditions for the students to do Space-related activities as a first-time experience; to supervise and assure the coordination between departments exists; to plan and assure the execution of the tasks. Achievements: more than 90 students interested in Space engaged in the team; 2 main projects; more than 4 main partners; formation of more than 15 departments. Skills: Project Planning · Interviewing · Space Technology · Sponsorship Relations · Entrepreneurship Team Photo Porto Space Team
INEGI driving science & innovation · Full-time
During my fellowship, I have been able to perform algorithmic development of optimization approaches based on Particle Swarm Optimization and its hybridizations in Fortran and Python, in order to solve multi-objective optimization problems. The following is a list of tasks I am currently working on: 1. a literature review paper concerning optimization algorithms and recent applications in composite structures; 2. papers concerning a novel methodology of applying PSO based on the concept of elitism; 3. papers concerning a hybridization approaches of hybridization techniques Skills: Robust Design · Research · Composite Structures · Optimization · Interpersonal Relationships
The Online CubeSat Concurrent Engineering Workshop 2022 is an ESA Academy training session during which university students are introduced to the concurrent design of a CubeSat mission. Guided by ESA experts, the students learn to use the Open Concurrent Design Tool and identify design drivers. Divided into teams, they first create a subsystem concept to later achieve an already identified mission concept, function tree and product tree, using concurrent engineering. The workshop helps to better prepare the university students that are planning to embark on a CubeSat project or are at the early stages of one. CCEW_22 - Transcript - Ricardo Fitas.pdf CCEW_22 - Certificate - Ricardo Fitas.pdf
During the academic year 2021/22, I was responsible for coordinating the Teacher In-Service Program of IEEE UP SB, with the following numbers (variation in parenthesis related to previous records of the program): - Organization of more than 17 activities (+183%); - Collaboration with more than 12 schools (+100%); - Participation of a total of 13 IEEE UP SB students (+63%); - A total attendance of more than 800 elementary- and high-school students (+718%); - A first-time collaboration between TISP and a research lab. All the numbers mentioned above are an all-time record. Some organizational difficulties happened, not only due to covid-19 but also due to the late start of academic activities. TISP21_22_2 TISP21_22_1
Werkzeugmaschinenlabor, WZL der RWTH Aachen
Prediction of Workpiece Quality in Fine-Blanking Vorhersage der Bauteilqualität für das Feinschneiden Digitalization and initiatives such as Industry 4.0 enforces the transition from analog process execution to the development of cyber-physical manufacturing systems that incorporate data collection and analysis to monitor and optimize manufacturing systems and processes. As a preliminary, traditional manufacturing systems, stamping process need to be equipped with sensor systems to acquire different types of data about the process. The conversion from data into informative values turns out to be a challenging aspect of cyber-physical systems with regard to the structure and amount of data that is available. The fine blanking processes in the context of an Internet of Production is considered as a use case to research the potential of increasingly available data. It is necessary to analyze the relationship between process parameters (such as material properties, temperature and other operation conditions) on a Punch-to-punch basis as well as general trends with regard to the force signals. Hence, an in-depth analysis of cyclic signals per-punch and parameters is essential for any fine blanking system. This thesis works with the feature space of punch signals and the feature space of material properties. The goal of this project thesis was to develop an accurate prediction model for fine-blanking workpiece quality feature, the die roll, and investigate the relationship of force signals to the resulting quality of the workpieces. Next to the shearing surface quality, the die-roll width and depth are one of the main quality issues in components produced in a fine blanking process. The data used in this work is obtained from four experiments executed on a fine-blanking press equipped with sensors to acquire the three important process forces, namely punch force, counter-punch force and blank-holder force. 1020 PA Fitas_cvers.pdf
The project consists of building a prediction model with the highest accuracy on the quality prediction of production line parameters, using the data retrieved from sensors. General Machine-Learning techniques and other Data Science-related knowledge are used for experiments for the accomplishment of the results. Using Sequential Feature Selection (1 feature) and Random Forest (RF) with 50 trees were enough to improve significantly R2-scores against AutoML on regression tasks (task 1: 1 against almost 0.5; task 2: 1 against almost 0.4; task 3: 1 against almost 0.85; task 4: 1 against almost 0.98). On segment prediction (classification tasks), Recursive Feature Elimination with Cross-Validation (RFECV, 1 feature) and RF turned true all the false classifications of AutoML (+100%). Knowing the feature to select, all the models are now created quicker than one millisecond, whereas AutoML took 200 seconds to create less-accurate models.
I attended the online "Introduction to Space Law Training Course", where general topics about space, future challenges, and general topics about the law were lectured. It had an exercise part that consisted of a United Nations debate simulation. ISLTC_21 - Transcript - Ricardo Fitas.pdf Introduction to Space Law Training Course 2021_certificates_Ricardo Fitas.pdf
Institut für Textiltechnik der RWTH Aachen University · Self-employed
My work aimed to optimise the starting point position of a Kinematic Draping Simulation, using Particle Swarm Optimisation. These developments are assisted with code optimisation and Parallel Computing and data visualization for a real application is displayed in a User Interface I developed as well. Internship ITA
During this period, I was responsible to help the Student Branch members with their tasks related to IEEE; to debate issues concerning the covid-19 pandemic, members online interaction and more general topics. I was the coordinator of the second "Fire Department" event of 2020/21, where students join to help each other with their academic projects and studying for the exams. This edition had +111% of participating students (74) compared to the previous edition (35).
Faculdade de Engenharia da Universidade do Porto · Self-employed
Development of knowledge on Optimisation history and methodology, mainly concerning Particle Swarm Optimisation and related hybridisation with other techniques; applications in Engineering areas - literature review; programming in Matlab and Python; Gears design optimisation: new design methodologies; writing of papers for journals and University of Porto internal conferences.
I was in a team caring about the high-school students' recruitment to form a team of talents before their entry into academic life. I participated in the organization of several activities for that team before its training sessions.
I was responsible for the foundation of AESS chapter in IEEE UP Student Branch since it would allow more students from different areas to participate and to be active members in IEEE activities. AESS was the group where most IEEE UP SB members worked, during my coordination (23 out of 54) and at the beginning of the next academic year, after my AESS coordination (12 out of 31). I was responsible for the project Tarvos during my leadership. Tarvos aims to develop a small rocket with 3D printing components, allowing the growth of knowledge in this field. With this project and visits to different research units at the University of Porto, I took an important role in the growth of IEEE UP SB activity even during the covid-19 pandemic.
Acquisition of case studies of Gas Water Heater users: - Big Data treatment: treatment of the behaviour of GWH based on data (temperature, flow) collected from data loggers application; Matlab scripts interpretation and improvement; computational optimization. - Analysis and correction of procedures for Durability laboratory. - Improvement of organizational skills: meetings, 5s digital, programming automatic file organization in their directories (VBA). - Matlab GUI interfaces creation for uniformization for scripts usage. - Presentation of numerical analysis of data. - Matlab validation based on VBA userforms with known information filled. - Databases and folders update related with field tests. - Data logger assembly: flow meters, hydraulic components, temperature sensors. - Test request improvement for Durability laboratory, based on proceeding standard configurations. Statement internship Bosch
- Analysis of carotid medical images; - Code development about medical images processing; - Numerical analysis applications; - Computer vision; - Writing articles; - Work presentation/speaker.
Instituto Superior de Contabilidade e Administração do Porto
- Researching works about usability/user experience; - writing of papers. CEOS internship certificate [PT]
The program consists of going to middle and high school to present the activities which are usually done at the University of Porto, especially related to Engineering courses. Aiming to encourage those students to gain interest in technology, programming activities and games were done in those activities. TISP17_18_1
At the beginning of my newbie phase in the IEEE UP Student Branch, I did some volunteering activities and helped my colleagues in the preparation of workshops and conferences. Also, I was the lecturer of some MATLAB workshops inside and outside of the University of Porto. Matlab_2 SYP_1 Matlab_1
Introduction to Topology Optimization with Prof. Ole Sigmund
Activities and societies: All the courses of this program are taught by MIT faculty and administered by the Institute for Data, Systems, and Society (IDSS) at a pace and level of rigor similar to that of an on-campus course at MIT. This program brings MIT’s rigorous, high-quality curricula and hands-on learning approach to learners worldwide—at scale. URL: http://credentials.edx.org/credentials/217fff3ea6624cbcbc4bb9f519cb1fca/ ID: 217fff3ea6624cbcbc4bb9f519cb1fca Skills: Statistics · Machine Learning · Social Sciences · Data Science · Probability Theory MicroMasters_Credentials.pdf
Erasmus+ study abroad (winter + summer semesters 2020/21) RWTH_Cert.pdf
Grade: 16 out of 20 (Grade: A) MSc-1.pdf
Grade: 14 out of 20 (Grade: B) BSc-1.pdf
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