Saeid Alireza Zadeh. Completed the Ph.D. in Mathematics in 2011 from the faculty of Science of Porto University. Is an Auxiliary Researcher at the Polytechnic Institute of Leiria. Published 11 articles in journals. Has 1 section(s) of books. Has received 1 award and/or honors. Works in the area(s) of Exact Sciences with emphasis on Mathematics with emphasis on Pure Mathematics, Exact Sciences with emphasis on Computer and Information Sciences with emphasis on Computer Sciences and Natural Sciences with emphasis on Biological Sciences with emphasis on Ecology. He is working on optimizing the performances of automated and unmanned vehicles used in fruit harvesting by reducing the localization and coordination errors, which are improvements on the adaptation of Project DBoidS "https://sciproj.ptcris.pt/171663PRJ" (PTDC/CCI-COM/2416/2021); developing strategies and metrics for optimizing dynamic task allocation in robotic network cloud systems; developing sensorless motion planning without collisions in deterministic environments; developing motion planning without collisions in dynamic environments; and developing a fast and efficient method to estimate the weights in training neural networks. He is a Project Developer Member at the Institute Superior de Agronomia in Lisbon, Portugal, he contributed to the successful funding of the "Ecological patterns and processes across scales: application to river networks" project by the European Funding Agency FCT. He played a crucial role in validating the proposal and model, comparing existing models, and collaborating with a diverse team of researchers. In his role as a Postdoctoral Researcher at the Cloud Computing Competence Center (C-4), UbiMed in Covilhã, Portugal, He demonstrated expertise in mathematical modeling and computer-implemented methods for optimizing and securing the allocation of algorithms in robotic network cloud systems. His contributions also extended to developing innovative approaches for optimal task scheduling in cloud environments and human-robot collaboration systems. His research encompasses areas such as ecological niches, task completion dependencies, task assignment in robotic network cloud systems, and determining optimal neural architecture. His academic achievements include receiving the Best Paper Award at the International Conference on Artificial Neural Networks in 2021 and the Best Paper Award at MDPI AG for publication of the paper: "Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal". He has also submitted a patent for a computer-implemented method of optimal and secure algorithm allocation to processing units. With a broad set of technical skills, including proficiency in programming languages such as Python, R, and SQL, and expertise in cloud computing, data analysis, and artificial intelligence, Saeid brings a multidisciplinary approach to his research. In addition to his academic and research achievements, he has obtained certifications in Data, ML, and AI in Google Cloud, Networking and Security in Google Cloud, Infrastructure in Google Cloud, Cloud Computing Fundamentals, JavaScript, SQL, and Python. He is a researcher with a diverse skill set and a track record of impactful contributions to the fields of mathematics, ecology, cloud computing, and robotics.