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Rickard Sörlin

Education

Formal depth built deliberately on top of practice

Built formal Computer Science and Applied AI capabilities on top of extensive professional experience in industrial digital platforms and connected systems.

Degree

Mälardalen University

Degree of Bachelor of Science in Computer Science with Specialization in Intelligent Systems

B.Sc. in Computer Science · Applied Artificial Intelligence

Built formal Computer Science and Applied AI capabilities on top of extensive professional experience in industrial digital platforms and connected systems.

Rickard Sörlin with thesis colleagues and examiners at Mälardalen University
Degree project completed — Mälardalen University

AI & Data

  • Probability & Statistics
  • Machine Learning
  • Advanced Machine Learning
  • Deep Learning
  • Artificial Intelligence 1 & 2
  • Natural Language Processing
  • Reinforcement Learning
  • Generative AI
  • Predictive Analytics
  • Data Science

Software & Data

  • Software Engineering
  • Software Engineering for AI
  • SQL
  • Cloud Platforms

Product / Human-Centred Development

  • Interaction Design
  • Agile development
  • User stories
  • Backlog structuring
  • UML
  • Requirements prioritisation

Responsible Technology

  • AI Ethics

The degree was completed before the postgraduate development period beginning in August 2025.

Postgraduate development

Advanced AI, Innovation & Product Development

A coordinated postgraduate development period across several universities, covering advanced artificial intelligence, Innovation Management, Industrial Economics, Product Management, Product & Requirements Management, Strategy & Business Models, Agile Process & Project Management and Leadership.

Phase 1 · Aug 2025 – Jan 2026 · Advanced AI Foundation

Aug 2025 – Jan 2026

Linköping University

Advanced-Level Studies in Artificial Intelligence: Natural Language Processing

Advanced-level studies covering Natural Language Processing, transformer architectures, domain adaptation, sentiment classification and PyTorch-based model implementation, with emphasis on adapting transformer models to domain-specific tasks using Parameter-Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA).

  • Natural Language Processing
  • Transformer architectures
  • Domain adaptation
  • Sentiment classification
  • PyTorch
  • Parameter-Efficient Fine-Tuning (PEFT)
  • Low-Rank Adaptation (LoRA)

Product relevance

  • Technical feasibility
  • Domain-specific AI
  • Understanding model limitations
  • Collaboration with AI engineering teams

Jan 2026 – Jun 2026

Umeå University

Advanced-Level Studies in Artificial Intelligence: Autonomous Systems & Perception

Advanced-level studies in autonomous systems and machine perception, covering multi-sensor fusion, 3D perception, Bird's-Eye View representations, LiDAR point-cloud processing, object detection, trajectory and motion forecasting, path planning and reinforcement learning for adaptive control.

Explored how perception, prediction, planning and control are integrated to enable data-driven intelligent systems to interpret dynamic environments and support real-time decision-making.

  • Multi-sensor fusion
  • 3D perception
  • Bird's-Eye View representations
  • LiDAR and point-cloud processing
  • Object detection
  • Trajectory and motion forecasting
  • Path planning
  • Reinforcement learning
  • Adaptive control

Product relevance

  • Data quality
  • Uncertainty
  • Sensor constraints
  • Real-time decisions
  • Safety
  • Model/system interaction

Completed Nov 2025

Mälardalen University

Predictive Data Analytics

Second-cycle studies in predictive data analytics, machine learning and prediction for decision support.

  • Predictive analytics
  • Machine learning
  • Prediction
  • Decision support

Completed Dec 2025

Mälardalen University

Deep Learning for Industrial Imaging

Second-cycle studies in deep learning and computer vision applied to industrial imaging.

  • Deep learning
  • Computer vision
  • Industrial imaging
  • Industrial AI

Bridge · Aug 2025 – Jan 2026 · Innovation Management

Aug 2025 – Jan 2026

University of Skövde

Advanced-Level Studies in Innovation Management

Focused on strategic innovation, applied innovation processes, implementation and innovation leadership.

Studied in parallel with the advanced AI foundation, this formed the innovation and product bridge developed further at Blekinge Institute of Technology.

  1. Advanced AI
  2. Innovation Opportunity
  3. Talking SCADA — Initial Concept
  4. Product / Requirements Development

Aug 2025 – Jan 2026

Course project — Talking SCADA (concept origin)

Developed the initial concept for Talking SCADA, exploring how AI and existing operational data from building systems could turn complex system information into understandable insights and decision support for facility managers and operators. The work established the innovation opportunity and concept foundation later developed further through product management and requirements work.

  • Strategic innovation
  • Applied innovation processes
  • Implementation
  • Innovation leadership

Phase 2 · Jan 2026 – Jun 2026 · Product · Industrial Economics · Requirements

Aug 2025 – Jun 2026

Blekinge Institute of Technology

Advanced-Level Specialization in Industrial Economics, Product & Requirements Management

Building on the technical AI and innovation foundation, this phase focused on how customer needs and technology opportunities become viable products, requirements, business models and sustainable value.

Advanced-level specialization covering Industrial Economics and Management, Strategy and Business Models in Technology-Intensive Businesses, Product Management, Product and Requirements Management for Digital Environments, Agile Process and Project Management, and Leadership in High-Technology and Knowledge-Intensive Organizations.

Aug 2025 – Jun 2026

Course project — Talking SCADA (product & requirements case)

Used Talking SCADA as a recurring academic product case to apply New Product Development, product discovery, digital product strategy and requirements engineering to an AI-enabled decision-support concept for building automation and SCADA/BMS environments. Applied methods across opportunity identification, product vision and value proposition, structured requirements management, feature prioritisation, MVP definition, product development governance, portfolio considerations, business model development and go-to-market planning — connecting customer needs and technical feasibility with sustainable business value.

Industrial Economics and Management

  • Business value
  • Technology and business perspective
  • Sustainable value

Strategy and Business Models in Technology-Intensive Businesses

  • Product strategy
  • Value proposition
  • Business model development
  • Market relevance
  • Go-to-market planning

Product Management

  • New Product Development
  • Opportunity identification
  • Product discovery
  • Product vision
  • Feature prioritisation
  • MVP definition

Product and Requirements Management for Digital Environments

  • Product Requirements Document
  • System-level requirements
  • Functional and non-functional requirements
  • Explainability and reliability
  • Requirements prioritisation
  • Technical feasibility

Agile Process and Project Management

  • Iterative concept refinement
  • MVP planning
  • Development planning
  • Product development governance

Leadership in High-Technology and Knowledge-Intensive Organizations

  • Stakeholder alignment
  • Communication
  • Cross-functional perspective

Wider institutions involved in the postgraduate AI specialisation include Umeå University, Linköping University and Mälardalen University.

Certifications

Additional programmes

  • AI Governance & Responsible AI

    University of Oxford

  • Machine Learning Specialization

    Stanford University Online

  • Deep Learning Specialization

    DeepLearning.AI

Foundation

Earlier technical foundation

  • IoT & Automation

    Sjödals Gymnasium

  • Robotic and Process Automation

    PLC · Robotics · Process Automation

Capability map

Full capability overview

Product & Strategy

  • Product Management
  • Product Strategy
  • Product & Portfolio Strategy
  • New Product Development
  • Product Discovery
  • Requirements Engineering
  • Requirements Prioritisation
  • Feature Prioritisation
  • MVP Definition
  • Roadmap Contribution
  • Product Lifecycle Thinking
  • Value Proposition
  • Business Models
  • Go-to-Market
  • Stakeholder Alignment
  • Technical-commercial trade-offs

AI & Data

  • AI-enabled products
  • GenAI / LLMs
  • RAG
  • GraphRAG
  • Agentic AI
  • Multi-Agent Systems
  • Machine Learning
  • Deep Learning
  • NLP
  • Predictive Analytics
  • Time-Series Forecasting
  • Probabilistic Forecasting
  • Computer Vision
  • Data-Driven Decision Support

Industrial / Platform

  • Industrial Digital Platforms
  • SCADA
  • BMS
  • HVAC
  • EMS
  • Building Automation
  • PLC
  • IoT
  • IIoT
  • OPC UA
  • Edge Systems
  • Connected Systems
  • Industrial Communication
  • Real-Time Monitoring
  • Energy Systems
  • Smart Buildings
  • Mission-Critical Environments

UX / Discovery

  • Customer Discovery
  • User Needs
  • Interaction Design
  • Wireframing
  • Paper Prototyping
  • Figma
  • User Testing
  • Accessibility
  • Inclusive Design
  • Heuristic Evaluation

Tools

  • Python
  • PyTorch
  • Scikit-learn
  • LangChain
  • LangGraph
  • LangSmith
  • RAGAS
  • Transformers
  • PEFT
  • LoRA
  • SQL
  • NoSQL
  • Graph Databases
  • Knowledge Graphs
  • Vector Databases
  • Time-Series Data
Depth varies across these areas — they are shown as working familiarity, not equal expert-level mastery.