SKills Development
Bridging the Skill Gap for Circular Future
In Wood2Wood we focus on identifying emerging roles and relevant skills, then we support the organizations involved in the use case experiments to address existing skills gaps by recommending training courses and re/upskilling pathways.
Our methodology is grounded in rigorous assessment, employing surveys and interviews to gauge skill maturity and progress. Finally, we translate these insights into practical application through interactive workshops and targeted training sessions, ensuring that the skills and jobs we cultivate are directly applicable and beneficial to real-world organisational settings.
Roles and Skills
To delve into the specific responsibilities and required proficiencies, please explore our Roles and Skills section. Under Use Cases (Waste Stream Management, UC#1, UC#2, and UC#3), their corresponding roles and skills have been highlighted. The Quick Links section below will help you navigate the options.
Quick Links
- Use Case: Waste Stream Management
- Use Case #1: CDW Treatment
- Use Case #2: Waste Management Treatment by Removing Additives
- Use Case #3: Integrated Waste Valorization Pathway
- Methodology and Results
- Glossary
- Visit Trends, Future Signals and Occupational Scenarios page
- Visit Resources page to download deliverables and reports
Use Case: Waste Stream Management
Waste Management and AI / Robotics Integration Engineer
This role is responsible for designing and implementing machine learning algorithms that enhance the sorting, characterization, and separation processes in waste management. This role includes developing models for waste classification, optimizing robotic operations, and collaborating with other engineers to integrate AI solutions into existing systems.
i. Expertise in developing and implementing machine learning algorithms specifically
tailored for waste classification, robotic sorting, and process optimization in waste management.
ii. Proficiency in integrating AI with robotic systems, enabling collaboration between human operators and robotic arms for enhanced sorting efficiency and operational performance.
iii. Advanced programming skills in Python and R, with a focus on data-driven model development for real-time waste sorting and characterization tasks.
iv. Skilled in analyzing complex and heterogeneous waste datasets to uncover patterns, improve the precision of waste classification models, and optimize separation processes.
Robotics Automation Engineer
This role is responsible for designing, developing, and maintaining robotic systems used in the sorting and separation of waste materials. This role involves working closely with AI developers to ensure seamless integration of robotics with machine learning algorithms, as well as conducting tests to optimize robotic performance.
i. Expertise in designing robotic systems tailored for specific waste management
applications.
ii. Proficiency in programming languages such as C++ or ROS (Robot Operating System)
for robot control and automation.
iii. Knowledge of control systems used in robotics for precise operation and automation.
iv. Familiarity with simulation tools (e.g., Gazebo, V-REP) for testing robotic designs virtually before physical implementation.
v. Experience with integrating sensors (e.g., cameras, LIDAR) that assist robots in identifying and sorting materials.
Use Case # 1: CDW treatment via cascade bio refinement with
separation lignin and cellulose
Polymer Process Engineer
The Polymer Process Engineer is responsible for developing and optimizing polymer processing techniques to achieve specific material properties and efficiency in product quality. This role extends to the chemical and mechanical processing of waste wood, where the engineer will manage the separation of lignin from cellulose fibers and the subsequent production of cellulosic nanofibers. The engineer will also contribute to scaling processes from laboratory to industrial environment and exploring data-driven techniques like machine learning for enhanced efficiency.
i. Strong understanding of structure-property relationships in polymers.
ii. Knowledge of biodegradable polymer composites, particularly PLA-based materials, and their applications in construction.
iii. Experience in scaling processes from laboratory to industrial environments.
iv. Familiarity with machine learning techniques for process optimization and predictive modeling.
v. Effective collaboration and communication skills for interdisciplinary teams.
vi. General skills in root cause analysis, continual improvement, and quality assurance.
ML Process Optimization Engineer (Extrusion Systems)
This role plays a pivotal role in the integration of ML/AI technologies into industrial material science processes such as polymer processing and extrusion. The engineer develops ML models to analyze operational parameters from extrusion processes and create predictive algorithms that can enhance material quality and process efficiency through real-time monitoring.
i. Expertise in machine learning and data-driven modeling for optimizing polymer extrusion processes.
ii. Experience applying ML techniques to manufacturing environments, particularly in polymer processing and extrusion.
iii. Proficiency in working with sensor data (e.g., pressure, temperature, torque) to enhance extrusion process efficiency and monitor operational parameters.
iv. Advanced data analysis and preprocessing skills for effective model development and
integration into extrusion processes.
Research Engineer, Polymer Additive Manufacturing
The Research Engineer, Polymer Additive Manufacturing focuses on the research and development of polymer- based materials and technologies for additive manufacturing (3D printing). This role involves the optimization of polymer formulations, printing processes, and post-processing techniques to achieve desired material properties and performance. The engineer works in collaboration with scientists, product developers, and manufacturing teams to push the boundaries of polymer-based 3D printing for both industrial and consumer applications.
i. Strong understanding of polymer chemistry and material science, particularly with thermoplastics and thermosetting polymers used in additive manufacturing.
ii. In-depth knowledge of additive manufacturing techniques (e.g., FDM, SLS, SLA), focusing on polymer-based printing processes.
iii. Familiarity with polymer-based 3D printing techniques and equipment.
iv. Ability to innovate new materials or technologies that improve the efficiency and performance of additive manufacturing.
v. Familiarity with CAD software (e.g., NX, SolidWorks, AutoCAD) and 3D printing slicer tools for preparing models and optimizing them for printing.
Use Case # 2: Waste management treatment by removing additives, via
advanced chem and bio techs
Chemical Analysis Specialist (PhD/Postdoc Level)
The Chemical Analysis Specialist conducts chemical and physical tests on materials (e.g., green glue, polyurethane) to ensure they meet quality standards. This role involves analyzing data from material characterization tests, collaborating with production teams to improve material quality, maintaining detailed records of test results, and ensuring compliance with industry regulations.
i. Advanced chemical analysis of material properties (e.g., NIPU glue, polyurethane, wood fibers): Ability to conduct detailed chemical and physical tests to ensure material quality and performance in industrial applications, using advanced tools such as Molecular Dynamics Software (e.g., LAMMPS, Materials Studio) for material interaction modeling, Finite Element Analysis (FEA) for performance simulation, HPLC for chemical component analysis, and data analysis software like MATLAB, or Python libraries (e.g., SciPy, Pandas).
ii. Expertise in analytical techniques such as liquid chromatography, mass spectrometry, and high- performance thin-layer chromatography (HPTLC): These techniques are used to analyze the chemical composition of wood fibers, adhesives, and composite materials in both lab and industrial settings.
iii. Proficiency in operating laboratory instruments for chemical analysis: Familiarity with instruments for testing chemical and physical properties of materials like green glue and wood composites, ensuring the accuracy of data collected.
iv. Skilled in interpreting material test results using statistical methods: Ability to apply statistical tools (e.g., ANOVA, regression analysis) to evaluate material quality and improve processes based on test data, supporting material optimization and compliance with industry standards.
Production Data Analyst (with AI/ML Integration Potential)
The Production Data Analyst is responsible for collecting, analyzing, and interpreting data from material testing, production processes, and operational performance, particularly in the context of steam explosion, polymerization, and wood fiber treatment. This role focuses on identifying inefficiencies and optimizing workflows by analyzing data patterns. Additionally, the analyst explores opportunities to integrate AI/ML models to predict outcomes and improve decision making processes in material science and production activities.
i. Proficiency in analyzing large datasets related to production performance: Skilled in evaluating production data (e.g., steam pressure, material composition, and operational metrics) to identify trends that impact efficiency and material quality.
ii. Expertise in statistical software (e.g., Python, R) for data analysis: Competence in using tools to process and analyze production and material testing data, generating actionable insights.
iii. Ability to identify inefficiencies based on data patterns: Expertise in detecting inefficiencies or bottlenecks in the production workflow (e.g., material processing times, equipment usage), enabling process improvements.
iv. Proficiency in applying machine learning algorithms (e.g., regression, classification, clustering): Skilled in using AI/ML techniques to develop predictive models that can forecast production outcomes and material performance, facilitating data-driven decision-making.
Use Case # 3: Integrated Waste Valorization Pathway for Bio-Based
Detergent Production: A Multi-Stage Thermochemical and
Biotechnological Process
Thermochemical Process Engineer (HTC and Gasification)
The Thermochemical Process Engineer is responsible for designing, optimizing, and monitoring hydrothermal carbonization (HTC) and gasification processes. This role focuses on conducting experiments and simulations to validate process designs, integrating AI and machine learning models to enhance process efficiency, and collaborating with cross functional teams to improve the production of biochar, syngas, and downstream products. The engineer ensures all processes comply with environmental regulations and safety standards relevant to high pressure, high-temperature operations in waste processing.
i. Expertise in designing and optimizing thermochemical processes such as hydrothermal carbonization (HTC) and gasification: Ability to lead experiments and improve process designs based on data analysis.
ii. Proficiency in using simulation tools for modeling chemical processes: Skilled in running simulations to validate the performance of HTC and gasification systems under various operating conditions.
iii. Advanced analytical skills for interpreting complex datasets: Skilled in analyzing operational data to identify bottlenecks and enhance process efficiency.
iv. Familiarity with high-pressure and high-temperature sensor and control systems: Experienced in managing the instrumentation and control systems needed for safe and efficient operation of HTC and gasification reactors.
v. Understanding of environmental regulations and safety standards: Well-versed in ensuring all process designs and operations adhere to industry standards for waste processing and sustainable energy production.
vi. Knowledge of integrating AI and machine learning models into process design: Ability to leverage AI/ML for optimizing thermochemical processes, improving automation, and enhancing predictive capabilities for biomass gasification.
Thermochemical Machinery Operator
This role is responsible for operating machinery used in HTC and gasification processes. This role focuses on conducting routine maintenance, adhering to safety protocols during operations, documenting operational parameters, and ensuring optimal performance of equipment.
i. Expertise in operating machinery specific to HTC and gasification processes.
ii. Proficiency in performing routine maintenance and troubleshooting mechanical issues: Ability to perform routine maintenance and resolve mechanical issues e effectively, with awareness of basic predictive models and tools to schedule predictive maintenance, ensuring timely interventions to enhance equipment reliability and reduce downtime.
iii. Awareness of basic AI tools that assist in monitoring equipment performance.
iv. Familiarity with recording operational data for performance analysis.
v. Understanding of safety standards for operating high-pressure systems.
Bioreactor Technician (Syngas-to-Detergent Process)
This role is responsible for managing bioreactor setups for detergent production from syngas. This role focuses on monitoring bioprocess parameters, optimizing conditions for microbial growth, collaborating with research teams to validate product quality, and maintaining accurate records of experimental procedures.
i. Proficiency in managing bioreactors, incorporating statistical models and AI/ML tools for optimizing fermentation parameters and predicting system performance.
ii. Understanding of microbial cultures and their applications in detergent production.
iii. Competence in using advanced data analytics tools and machine learning algorithms for real-time monitoring, anomaly detection, and performance optimization in bioprocesses.
iv. Experience with laboratory equipment and procedures related to bioprocessing.
v. Skill in adjusting bioreactor conditions (e.g., pH, temperature), leveraging predictive models and AI tools to forecast optimal environmental parameters and maximize yields.
vi. Expertise in chromatography with integration of AI-enhanced data processing and statistical analysis to improve accuracy and streamline product characterization.
Methodology and Results
The 6Ps methodology, implemented in the Wood2Wood project, was originally developed by Politecnico di Milano as part of the Horizon 2020 MIDIH project. This structured framework is designed to assist organizations in assessing their current digital maturity, establishing target maturity levels within a defined time horizon, and subsequently formulating a roadmap to guide their digital transformation.
The methodology is built upon six core dimensions—collectively known as the 6Ps—which encompass three technical pillars (Product, Process, Platform) and three socio-organizational pillars (People, Partnership, Performance). The objective of this framework is to enable effective digital transformation strategies by evaluating each dimension and identifying the necessary tools, services, and competencies required to achieve the predefined objectives. Within WP16 (specifically Deliverable D16.2, Task 16.2), the methodology has been employed with particular emphasis on the People dimension. The main focus lies in evaluating and advancing education and skills development in the context of digitalization.
This dimension-specific analysis involves a detailed investigation of job functions and roles across different Use Cases within the project. Crucially, the approach goes beyond evaluating existing roles; it is also designed to identify and define new professional roles and emerging skillsets that may not yet be embedded within participating organizations
but are projected to become essential in the near future. These new roles are indicative of the evolving demands brought about by digital transformation, where traditional positions may need to be restructured or supported through new competencies.
Steps
To operationalize the methodology, a systematic multi-step approach was adopted.
Initially, interviews and qualitative assessments were conducted across all Use Cases, focusing on specific operational contexts and organizational needs. Drawing from this data, relevant roles and competencies were defined by mapping domain-specific activities and aligning them with established references, such as those from Politecnico di Milano’s Osservatori Digital Innovation, the “Skills for Industry 4.0” framework (Pinzone et al., 2023), and other pertinent academic sources.
Subsequently, two targeted surveys were developed. The first survey aimed to prioritize the identified skills associated with each role, relying on the practical insights and experiences of project partners. The second survey sought to measure the current (AS-IS) and anticipated (TO-BE) status of these roles and skills within each organization. To enrich and validate the data collected, a dedicated workshop was conducted with both internal and external stakeholders.
This collaborative session facilitated the refinement of findings and offered a deeper understanding of emerging workforce needs.
Finally, relevant training courses were identified and examined in relation to the validated roles and skillsets. These courses were classified into three levels—Awareness, Foundation, and Extended Know-How—ensuring that they address diverse organizational contexts and training requirements.
Results
To obtain further information regarding the analysis steps, workshop outcomes, and detailed training courses, please refer to the full report of Deliverable 16.2.
Glossary
Skills
According to the European Parliament Council, skills are defined as the ability to apply knowledge and use know-how to complete tasks and solve problems.
Cognitive skills
Skills can be described as cognitive, that is, “involving the use of logical, intuitive, and creative thinking”.
Practical skills
Skills can be described as practical, that is, “involving manual dexterity and the use of methods, materials, tools, and instruments.”
Role
A role is a set of responsibilities, tasks, or functions assigned to a person or position within a
specific context or organization.
Competence
Competence means the proven ability to use knowledge, skills and personal, social and/or
methodological abilities in work, study situations and in professional and personal development.
Up-Skilling
• Refers to the practice of acquiring new skills or enhancing existing skills to stay competitive in the job market. Upskilling is specifically focused on obtaining knowledge, expertise, or capabilities related to your current field or industry in order to advance your career or adapt to changes in the job market.
• Upskilling means learning new and enhanced skills that relate to your current role. Think about it as “leveling up” your skills.
Reskilling
Refers to the acquisition of entirely new skills. Reskilling is often pursued to pivot to a different
career or industry due to changes in job demand or personal career goals.