About this positionThe energy transition requires advanced optimization methods to manage increasingly complex and flexible energy systems. In this internship, you will explore how emerging computational approaches, including hybrid quantum–classical optimization methods, can support future energy system planning and operation.You will work with a multidisciplinary team at TNO on practical optimization challenges and assess where advanced solution techniques can provide value beyond conventional approaches. The internship offers a unique opportunity to work at the intersection of energy systems, optimization, and emerging computational technologies.What will be your role?During this internship, you will work on the development and evaluation of optimization methods for representative energy system planning or scheduling problems.Typical activities include:Formulating an energy system optimization problem.Developing and implementing a classical optimization baseline.Investigating advanced decomposition approaches such as column generation, Benders decomposition, or related methods.Exploring hybrid quantum–classical optimization workflows where appropriate.Benchmarking solution quality, computational performance, scalability, and practical applicability.Translating findings into clear technical conclusions and recommendations.You will contribute to ongoing research activities within TNO and gain hands-on experience with optimization, energy systems modeling, and scientific software development.What we expect from youFor this internship, we are looking for a Master's student with a background in:Electrical EngineeringApplied PhysicsComputer ScienceOperations ResearchMathematicsor a related fieldPreferred qualifications:Solid background in mathematical optimization (e.g. MILP, operations research, convex optimization, or decomposition techniques).Experience with Python and scientific computing.Interest in energy systems and advanced optimization methods.Interest in quantum computing is a plus, but not required.Ability to work independently and communicate technical results clearly.We are looking for a curious and proactive student who enjoys solving challenging technical problems and contributing to applied research.Duration: preferably 5–6 months, with some flexibility depending on university requirements.What you'll get in returnAn internship at TNO means working in an environment where substance and impact are central. You will become part of a knowledge organisation where research and practice come together, and where experts collaborate on solutions to current societal and technological challenges.Your internship is a period in which you can discover what suits you, where your strengths lie and what you would like to learn next. You are part of a professional working environment, gain insight into how things work in practice, and have the opportunity to build experience that goes beyond this internship alone. For many students, an internship is therefore also a first step in discovering whether TNO could be a potential next step after graduation.In addition, we offer you:A professional and innovative internship environment in which you actively contribute to societal and technological challenges, working alongside leading experts in your field.Personal and dedicated supervision, with focus on your learning objectives, development and study assignment.Room to develop: you gain relevant work experience, develop both your subject‑specific and professional skills, and build a valuable network.Use of a laptop and the facilities you need to perform your work effectively.A monthly internship allowance of € 615 for a full-time internship, for MSc, BSc and vocational education (MBO) students.Up to eight hours of leave per internship month for a full-time internship, allowing you to balance your internship with your studies and personal life.A contribution towards travel expenses if you are not entitled to a student travel card.A free membership to Jong TNO: the network for young colleagues, where you can meet other TNO colleagues and participate in sports activities, professional and personal development activities, and social events such as the annual ski trip.TNO as an employerOur people are at the heart of TNO. Their curiosity, expertise and entrepreneurial mindset make it possible to deliver high-impact research and innovations that contribute to society’s sustainable wellbeing and prosperity. That is why we invest in an inspiring and inclusive working environment where colleagues can excel, have autonomy and continue to grow.Your talent and ambition have every opportunity to flourish at TNO. You work with experts (both within and beyond TNO), have access to advanced technology and the freedom to explore, experiment and innovate. Our strength lies in independence, reliability and collaboration. We find each other in wonder and ingenuity. We are driven to push boundaries. By working with businesses and government, and by connecting different perspectives, we strengthen our innovative capability and create responsible, meaningful results.At TNO, we believe this is our time to help society, government and business move forward faster. Together with driven colleagues, you turn knowledge into concrete innovations or ventures that truly make a difference by combining the power of science and entrepreneurship. And in doing so, you make your mark on our time.The selection processYou can apply for this internship position until the 30th of June, 2026. After submitting your application, you will receive a confirmation by email.TNO will arrange an appropriate internship agreement. If you have any questions about the content of the internship or the application procedure, you can contact the person listed below.If you start an internship with us, we will also ask you to provide a Certificate of Conduct (VOG).Important to know before applying:Before the start of the internship, an internship agreement must be signed. For students enrolled at a Dutch university or university of applied sciences, TNO uses the UNL-template, supplemented with several TNO‑specific agreements. For students from foreign educational institutions and MBO (secondary vocational education) programmes, the TNO internship agreement applies. TNO does not sign any other internship agreements.Before the start of the internship, the educational institution will need to confirm in writing that:you are enrolled at the educational institution for the duration of the internship, and;the internship forms part of your study programme.The confirmation of educational institution takes place by signing the UNL template or forms prepared by TNO.Interns at TNO must have a registered residential address in the Netherlands at the start of the internship. Carrying out internship activities from abroad is not possible.Are you an international student? Please note that you must have a BSN (Dutch citizen service number) before the start of your internship. Applying for a BSN can take several weeks, so make sure to start this process well in advance.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of hybrid quantum-classical optimization frameworks represents a critical milestone in the applied algorithmic layer, particularly for multi-scale industrial applications. As the energy transition accelerates, classical computing architectures face severe scaling limits when balancing increasingly decentralized and variable systems. Bridging quantum capability with classical mathematical programming provides a vital translation pathway to address these high-dimensional computational bottlenecks. This specialized role type functions as a crucial bridge within the early-stage talent pipeline, driving foundational enablement and benchmarking methodologies. By assessing where emerging computational workflows offer measurable algorithmic utility over conventional approaches, this role mitigates structural risks in long-term technology roadmaps. Consequently, fostering entry-level expertise in these co-design spaces is essential for scaling enterprise readiness and ensuring future cross-functional integration across the deep tech ecosystem.
Current industry focus lies on bridging classical and quantum capabilities at scale, transitioning from pure hardware prototyping toward practical application enablement. Within the broader quantum software value chain, specialized optimization research serves as a high-leverage entry point for heavy industries requiring real-time infrastructure coordination. The integration of high-performance computing (HPC) with early-stage quantum processors remains a primary bottleneck, as software toolkits and benchmarking protocols vary significantly across vendors. This technical fragmentation requires rigorous empirical analysis to validate algorithmic scalability and reproducibility before committing significant capital expenditures.
Ecosystem initiatives are increasingly targeting the development of hybrid workflows that deploy classical decomposition strategies alongside noisy, intermediate-scale quantum algorithms. Public funding cycles and national technology strategies across the OECD place a premium on validating these hybrid methodologies within critical infrastructure sectors like smart grids and logistics networks. Rather than relying solely on future fault-tolerant hardware, the current macroeconomic trend favors immediate, hardware-agnostic middleware optimization. This shift requires a continuous influx of specialized research talent equipped to navigate both mathematical programming and early-stage quantum compilers.
However, a pronounced workforce scarcity persists at the intersection of domain-specific industrial models and advanced computational physics. Junior and academic research positions serve as the primary mechanism for mitigating this talent mismatch by standardizing cross-disciplinary training methodologies. By establishing standardized benchmarks for algorithmic throughput, these positions reduce long-term integration friction and help the deep tech ecosystem move systematically through progressive Technology Readiness Levels (TRLs).
The capability architecture for this role type centers on the synthesis of traditional mathematical programming and emerging cloud-native quantum software layers. Proficiency across classical operations research frameworks, specifically mixed-integer linear programming and advanced matrix decomposition strategies, is essential for establishing baseline computational parameters. These classical foundations are systematically coupled with scientific computing environments to enable rigorous benchmarking of solution quality, execution latency, and resource scalability.
Understanding the interface points between classical high-performance computing resources and quantum accelerators is critical for driving architectural interoperability. This requires familiarity with hardware-agnostic development toolkits and hybrid orchestration protocols that manage the data throughput requirements of complex systems. By combining numerical analysis with structural problem formulation, this capability domain ensures that theoretical breakthroughs are directly translatable into enterprise software frameworks.
These cross-functional skills directly impact ecosystem throughput by reducing the iteration lag between academic theory and applied industrial deployment. Developing robust validation frameworks at this tier allows organizations to systematically assess the computational thresholds where hybrid workflows outperform classical baselines. Ultimately, these capabilities provide the foundational scaffolding required to integrate quantum-enhanced kernels into legacy enterprise architectures without disrupting core operational stability. - Accelerates the transition of hybrid quantum-classical optimization models from laboratory blueprints to applied industrial testbeds
- Mitigates technical execution risks by validating early-stage algorithmic benchmarks against established classical programming baselines
- Enhances long-term capability mapping for critical infrastructure sectors facing high-dimensional computational bottlenecks
- Cultivates a pipeline of specialized technical talent fluent in both traditional operations research and emerging quantum software layers
- Facilitates the standardization of performance metrics for hybrid workflows across fragmented quantum cloud platforms
- Shorter iteration cycles between academic algorithmic discoveries and functional industry-specific implementation frameworks
- Supports regional technology strategies by anchoring advanced computational research within verifiable European energy systems data
- Reduces integration friction between cloud-native quantum middleware and legacy enterprise resource planning systems
- Validates the commercial utility of advanced decomposition techniques before full-scale capital allocation into quantum hardware
- Promotes structural reproducibility across scientific computing frameworks by implementing uniform benchmarking protocols
- Strengthens cross-functional collaboration between domain-specific engineers, applied mathematicians, and quantum information scientists
- Lowers entry barriers for industrial organizations exploring quantum-as-a-service deployment models for large-scale scheduling applicationsIndustry Tags: Hybrid Quantum-Classical, Mathematical Optimization, Energy Systems Engineering, Algorithmic Benchmarking, Software Integration, Operations Research, Deep Tech Talent Pipeline, Technology Readiness Levels, High-Performance Computing
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