OBS Entropy Labs
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Overview
At OBS MIRD Entropy Labs, we are dedicated to advancing the frontiers of artificial intelligence, machine learning, and computational research to solve complex scientific and technological challenges. As a Computational Intelligence Scientist, you will play a pivotal role in developing cutting-edge computational models, algorithms, and systems that bridge the gap between theoretical insights and practical applications. Your work will contribute to various interdisciplinary domains including data science, optimization, robotics, and predictive analytics.
Position Overview
The Computational Intelligence Scientist will focus on designing, implementing, and optimizing intelligent computational systems for a variety of real-world applications. This role involves leveraging state-of-the-art techniques in artificial intelligence, machine learning, and optimization to drive research and development in solving complex problems across industries such as healthcare, finance, materials science, and energy. The role also entails collaboration with interdisciplinary teams to translate computational innovations into impactful, scalable solutions.
Key Responsibilities
1. Development of Computational Models
Design and develop advanced computational models using machine learning, deep learning, evolutionary algorithms, and reinforcement learning for solving complex problems.
Apply computational intelligence techniques to develop predictive and optimization systems that can handle large datasets and high-dimensional data.
Create novel algorithms for real-time decision-making and dynamic system optimization in various domains.
2. Data Science and Machine Learning
Utilize machine learning frameworks and tools to build and train predictive models, including supervised and unsupervised learning algorithms.
Perform data cleaning, feature engineering, and model evaluation using statistical and probabilistic methods to optimize model performance.
Collaborate with data scientists and domain experts to ensure the development of models that meet specific domain needs (e.g., natural language processing, computer vision, or anomaly detection).
3. Research and Innovation
Conduct cutting-edge research in computational intelligence, exploring novel algorithms and techniques for solving real-world challenges.
Explore advancements in AI/ML, including neural networks, evolutionary computation, and hybrid intelligence models.
Develop prototypes and proof-of-concept systems that showcase the effectiveness of novel computational methods.
4. Interdisciplinary Collaboration
Collaborate with experts from various domains such as physics, materials science, biology, and engineering to integrate computational intelligence solutions into complex scientific research projects.
Work closely with stakeholders to understand domain-specific challenges and translate them into computational frameworks that provide actionable insights.
5. Model Deployment and Optimization
Design, optimize, and deploy computational models for real-world applications, ensuring seamless integration with existing systems and workflows.
Ensure robustness, scalability, and maintainability of computational solutions across diverse domains and environments.
Conduct rigorous testing, validation, and performance analysis of deployed systems, with a focus on continuous improvement.
6. Publication and Knowledge Dissemination
Publish research findings in high-impact journals and present at scientific conferences and industry forums.
Actively contribute to the dissemination of knowledge within the computational intelligence community through presentations, workshops, and collaborative research initiatives.
Ideal Candidate Profile
Educational Background: Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computational Science, or a related field with a focus on computational intelligence.
Experience: Proven expertise in developing and implementing intelligent systems, optimizing computational processes, and applying machine learning techniques to complex data sets.
Technical Skills: Strong proficiency in programming, algorithm design, and data modeling with a focus on real-time and scalable solutions.
Key Focus Areas
Machine Learning & Deep Learning: Developing advanced models for prediction, classification, clustering, and feature extraction.
Optimization & Evolutionary Computation: Designing algorithms for dynamic optimization problems and decision-making under uncertainty.
Hybrid Intelligence: Integrating computational intelligence techniques with traditional rule-based systems and symbolic reasoning for enhanced outcomes.
Interdisciplinary Applications: Bridging computational intelligence with areas such as materials science, bioinformatics, and environmental modeling.
Domain-Specific Expertise
Experience in computational intelligence for scientific research, including fields like materials science, genomics, environmental science, or robotics.
Proven track record of interdisciplinary research and collaboration on projects involving computational methods in diverse domains.
Publication and Collaboration
Demonstrated expertise in publishing research outcomes in top-tier journals and contributing to collaborative research initiatives with academia and industry.
Project Leadership
Experience leading research projects or teams focused on the development and deployment of computational intelligence solutions for large-scale problems.
Special Domain Requirements
Expertise in computational optimization and decision-making systems for multi-agent and real-time systems.
Advanced knowledge of AI/ML methods applied to complex scientific data, including big data analytics and high-performance computing (HPC).
Ability to develop and apply hybrid intelligence systems combining symbolic reasoning with machine learning techniques for complex problem-solving.
Responsibilities
As a Computational Intelligence Scientist at OBS MIRD Entropy Labs, you will be responsible for developing and applying advanced computational techniques to address complex scientific and technological challenges. Below are the detailed technical responsibilities for this role:
Technical Responsibilities
1. Development of Advanced Computational Models
Design, implement, and optimize state-of-the-art computational models using machine learning, deep learning, evolutionary algorithms, and reinforcement learning.
Develop predictive models and optimization frameworks to handle large and complex datasets for diverse applications such as data analysis, decision-making, and real-time system optimization.
Research and apply hybrid intelligence techniques combining symbolic reasoning with machine learning to solve real-world challenges.
2. Machine Learning and Artificial Intelligence Development
Build, train, and fine-tune machine learning models for classification, regression, clustering, and feature extraction.
Implement deep learning architectures including CNNs, RNNs, LSTMs, and GANs for image recognition, time series analysis, and natural language processing (NLP).
Utilize advanced supervised and unsupervised learning algorithms to extract meaningful patterns from complex, high-dimensional datasets.
3. Optimization and Simulation
Develop optimization algorithms for dynamic, multi-objective, and constraint-based decision-making problems.
Apply techniques such as genetic algorithms, particle swarm optimization, simulated annealing, and other heuristic methods to solve real-world optimization challenges.
Design and conduct simulations for complex systems to predict outcomes and provide actionable insights for resource management and problem-solving.
4. Data Engineering and Analysis
Preprocess, clean, and preprocess large datasets for use in computational intelligence frameworks.
Perform statistical analysis and machine learning model validation, including accuracy, precision, recall, and F1-score evaluations.
Collaborate with data engineers and scientists to extract, transform, and load (ETL) data for research and development purposes.
5. Interdisciplinary Research and Collaboration
Work with domain experts from fields like physics, biology, chemistry, and engineering to develop computational solutions tailored to specific scientific challenges.
Translate domain-specific research problems into computational frameworks that can be modeled, analyzed, and optimized through computational intelligence techniques.
Collaborate on projects to create intelligent systems capable of handling real-world problems, such as material design, healthcare diagnostics, and environmental modeling.
6. Model Deployment and Performance Tuning
Deploy computational models in cloud or on-premise environments, ensuring scalability, performance, and robustness.
Conduct rigorous testing and validation to ensure that deployed models meet required accuracy and efficiency standards.
Continuously monitor and optimize model performance through regular updates and fine-tuning using new data and advanced computational techniques.
7. Publication and Knowledge Dissemination
Publish research findings in high-impact journals and conferences in the fields of artificial intelligence, machine learning, and computational science.
Present findings at national and international conferences, workshops, and industry forums.
Actively contribute to the dissemination of knowledge in computational intelligence through lectures, workshops, and collaborative research projects.
Qualifications
Required Qualifications
Educational Background
Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computational Science, or a closely related field with a specialization in computational intelligence.
Technical Skills
Machine Learning & Deep Learning:
Expertise in building and training machine learning models using frameworks like TensorFlow, PyTorch, or Keras.
Optimization Techniques:
Strong experience with optimization algorithms (e.g., genetic algorithms, simulated annealing, particle swarm optimization).
Programming:
Proficient in programming languages such as Python, R, Java, or similar, with a deep understanding of data manipulation and analysis tools (e.g., NumPy, Pandas, scikit-learn).
Data Handling and Engineering:
Hands-on experience in data engineering tasks, including data preprocessing, feature extraction, and large-scale data processing.
Research and Analytical Skills
Strong ability to conceptualize and implement computational solutions for scientific and engineering applications.
Excellent problem-solving and analytical skills, especially when dealing with ambiguous and complex research challenges.
Preferred Qualifications
Domain-Specific Expertise
Demonstrated experience applying computational intelligence in interdisciplinary fields such as materials science, bioinformatics, environmental science, or robotics.
Experience with computational techniques in quantum computing, financial modeling, or cybersecurity.
Publication and Collaboration
Proven track record of publishing research outcomes in leading journals and contributing to collaborative research efforts involving academia and industry.
Experience working with multi-disciplinary teams to solve scientific and technological challenges using advanced computational methods.
Project Leadership
Ability to lead research projects or teams focused on developing intelligent systems for large-scale applications.
Strong leadership skills for managing collaborative projects and ensuring timely delivery of computational solutions.
Special Domain Requirements
Expertise in developing real-time, large-scale decision support systems using machine learning and artificial intelligence.
Advanced knowledge of integrating AI/ML with high-performance computing (HPC) for scientific simulations and data-intensive applications.
Experience in deploying and maintaining intelligent systems in cloud environments or on-premise clusters with scalability in mind.
Special Domain Requirements
In addition to the core responsibilities and qualifications, the Computational Intelligence Scientist position at OBS MIRD Entropy Labs requires specialized expertise in certain areas to ensure effective contribution to cutting-edge research and development. Below are the special domain requirements:
Special Domain Requirements
1. Advanced Computational Optimization
Strong expertise in developing and applying advanced computational optimization methods for complex systems with multiple objectives and constraints.
Experience in solving real-world problems involving dynamic optimization, combinatorial optimization, and multi-criteria decision-making (MCDM) using evolutionary algorithms and heuristics.
2. Hybrid AI and Interdisciplinary Integration
Proven experience integrating hybrid AI techniques combining machine learning, symbolic reasoning, and knowledge-based systems for intelligent decision-making.
Ability to apply computational intelligence to interdisciplinary fields such as materials science, bioinformatics, environmental modeling, and quantum systems.
3. High-Performance Computing (HPC) and Cloud Deployment
Expertise in deploying computational models in high-performance computing (HPC) environments and cloud-based platforms (e.g., AWS, Google Cloud, Azure) for scalable processing.
Knowledge of optimizing machine learning models for deployment on distributed systems with real-time processing capabilities.
4. Model Robustness and Uncertainty Management
Strong background in designing models that handle uncertainty, noise, and incomplete data to ensure model robustness and reliability in real-world applications.
Experience in developing adaptive models that adjust dynamically to changing conditions and data streams.
5. Intelligent Systems for Scientific Research
Experience with AI-driven scientific discovery and simulation for solving challenging problems in scientific domains such as materials design, drug discovery, and molecular simulations.
Ability to apply AI/ML techniques to process and analyze large-scale scientific datasets for extracting meaningful insights.
6. Explainable AI and Interpretability
Proficiency in building models that are explainable and interpretable, ensuring that stakeholders can understand and trust the outcomes generated by AI systems.
Demonstrated knowledge of methods to enhance model transparency and fairness in computational intelligence applications.
These specialized domain requirements ensure that the Computational Intelligence Scientist position at OBS MIRD Entropy Labs attracts candidates with the necessary expertise to drive innovation and solve complex, real-world challenges through advanced computational methods.
