1- Developing AI Models:
* Model Creation: Designing and building machine learning models to solve specific problems.
* Model Training: Training models using large datasets to ensure accuracy and efficiency.
* Model Deployment: Transforming models into APIs for integration with other systems.
2- Data Management:
* Data Collection: Gathering and preprocessing data from various sources.
* Data Cleaning: Ensuring data quality by removing inconsistencies and errors.
* Data Storage: Managing databases and data warehouses to store large volumes of data.
3- Statistical Analysis:
* Data Analysis: Using statistical methods to analyze data and extract meaningful insights.
* Performance Metrics: Evaluating model performance using metrics like accuracy, precision, recall, and F1 score.
* Optimization: Fine-tuning models to improve performance and efficiency.
4- Collaboration:
* Cross-functional Teams: Working with data scientists, software engineers, and product managers.
* Stakeholder Communication: Explaining complex AI concepts to non-technical stakeholders.
* Project Management: Leading AI projects from conception to deployment.
1- Programming:
* Languages: Proficiency in Python, R, Java, and C++.
* Frameworks: Experience with TensorFlow, PyTorch, and Scikit-learn.
2- Mathematics:
* Statistics: Understanding probability, hypothesis testing, and statistical modeling.
* Linear Algebra: Knowledge of vectors, matrices, and tensor operations.
* Calculus: Familiarity with differentiation and integration for optimization algorithms.
3- Big Data Technologies:
* Tools: Knowledge of Apache Spark, Hadoop, and MongoDB.
* Data Processing: Experience with ETL (Extract, Transform, Load) processes.
4- Machine Learning Algorithms:
* Supervised Learning: Techniques like linear regression, decision trees, and support vector machines.
* Unsupervised Learning: Algorithms such as k-means clustering and principal component analysis.
* Deep Learning: Understanding neural networks, convolutional neural networks (CNNs), and recurrent neural n
Prince Sultan Defense Studies and Research Center (PSDSARC) was established as part of restructuring of the Saudi defense ecosystem to support the national military industries and to contribute to the Saudi Vision 2030 in achieving 50% localization of the Kingdom's military spending and enhancing national sovereignty over defense systems. The Council of Minister's Resolution no. 339, dated 16/8/1437H, approved the organization of PSDSARC as a government entity with an independent legal identity that organizationally reports to the General Authority for Defense Development (GADD). PSDSARC was also mandated in this resolution to enhance and support the Kingdom's defense and security future directions by conducting and developing qualitative and technical research related to defense, security, and strategic fields. Due to the crucial importance of R&D in the defense industry and also due to its important role as a link between operational requirements and technical solution development addressing traditional and modern threats, one of PSDSARC's key objectives is development of the qualified human capital that is capable of conducting R&D in highly specialized technology areas. As on one hand, the availability of laboratories is a major part of the research infrastructure, on the other hand, recruitment, training, mentoring and development of national talents are considered by PSDSARC as a key part of the R&D system across all its projects and programs. National sovereignty over defense systems constitutes a strategic goal closely associated with the ability to build national capabilities and competencies to support localization and development of modern technologies.