PhD Position in Predictive Planning & Hybrid AI for High-Mix, Low-Volume Manufacturing (Industrial Doctorate with Thales)

We are looking for a highly motivated PhD candidate to advance predictive, data-driven production and materials planning incorporating the high variability in high-mix, low-volume (HMLV) manufacturing environments. This position is part of an Industrial Doctorate, meaning the candidate will spend part of the PhD physically at Thales, working directly with planners, engineers, and data teams on real production challenges.

The PhD will be hosted by the Industrial Engineering and Management Sciences (IEMS) section in the High-tech Business & Entrepreneurship (HBE) department of the University of Twente.

This PhD position, designed with Thales NL, as an industrial doctorate position is part of the Predictive Plan programme, aimed as a flagship project for changing planning and scheduling in high mix low volume (HMLV) production by leveraging hybrid AI, data-driven workload estimation, intelligent release planning, and explainable decision support.

The PhD will operate across two worlds:

• The University of Twente — advancing scientific models, algorithms, and hybrid AI methodologies;
• Thales (the industrial partner) — applying these methods to real planning processes, data systems, and production settings.

The successful candidate will be co-supervised by Dr. Engin Topan.

In high-mix, low-volume manufacturing, traditional planning often fails to capture workload variability, uncertainty, and the complex interaction between product features, labor availability, and machine capacity.

Your PhD will address these challenges by:

• Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based modelling, and classical OR);
• Designing intelligent release, workload control and material planning methods that stabilize flow, improve on-time delivery, and reduce firefighting;
• Integrating uncertainty modelling and dynamic material planning and production and scheduling into an actionable decision-support toolkit;
• Embedding explainable AI to ensure planners and engineers understand, trust, and use the models;
• Developed hybrid AI solutions need to be developed and implemented locally as cloud-based solutions are not an option for Thales NL;
• Working on-site at Thales to:
• analyse real data,
• observe current planning practices,
• validate models in industrial settings,
• co-create practical tools with planners and engineers.

This close industrial involvement is a unique feature of this position.

Industrial Doctorate structure

As part of the Industrial Doctorate:

• You will spend 2 days per week at Thales (Hengelo).
• You will work closely with:
• production planning teams,
• operations engineering,
• data & analytics departments.
• You will help translate academic insights into real tools, workflows, and prototypes.
• You will maintain strong academic ties through weekly interactions at the University of Twente with your supervisors and research group.

This dual setting ensures that the PhD outcomes have both high scientific value and direct industrial impact.

Internal Service Employee

Binnen de afdeling Interne Dienst van Campus & Facility Management is een vacature ontstaan voor een flexibele en enthousiaste Medewerker Interne Dienst.

• Jouw functie vormt een combinatiefunctie met de functie van Collegezaalbeheerder en je werkt nauw samen met de Servicedesk van het gebouw.
• Je zorgt ervoor dat de klant gebruik kan maken van een functioneel, bruikbaar, veilig, schoon en toegankelijk (gedeelte van een) gebouw. Enerzijds door het verrichten van passende maatregelen hiertoe, anderzijds door het proactief signaleren van tekortkomingen.
• Jouw functie is niet gebouw gebonden.
• Je ressorteert zowel hiërarchisch als functioneel onder de Facilitair Teamleider van het gebouw, alwaar de functie wordt uitgeoefend.

Postdoc position on Federated/Continual Learning for Time-Series IoT Data (TRUMAN Project)

In this role, you will address the intricate challenge of enabling AI to learn continuously and collaboratively from wearable or mobile sensor data without compromising user privacy. Your efforts and collaborations with other European Union partners will contribute to the advancement of privacy-preserving artificial intelligence for the benefit of humanity.

What You Will Do:

• Research (Federated Continual Learning): You will develop novel and privacy-preserving algorithms that allow distributed devices (smartphones, wearables) to learn from new data streams over time (Continual Learning) while collaborating globally (Federated Learning).
• Analyze Mobile & Wearable Data: You will work with noisy, high-frequency time-series sensor data (e.g., accelerometers, gyroscopes, biosignals) from mobile IoT devices. You will address specific challenges such as user heterogeneity, concept drift in human behavior, and the need for personalization.
• Resource-Efficient AI: You will ensure your models are lightweight enough to run on edge devices, balancing accuracy with energy consumption and computational constraints.
• Project Coordination: You will assist the PI in managing work package tasks. This includes tracking technical progress, coordinating with partners on use-case requirements, and contributing to project deliverables.

About the TRUMAN Project:
The goal of TRUMAN is to design and develop generic technologies and methodologies for improving AI systems’ resilience against security, privacy, and fairness attacks, as well as to increase the trust that their users have in these systems, while accounting for different phases of the AI life cycle, starting from data collection through training and deployment. Your specific focus (Work Package 3) is on Human-In-The-Loop AI and Continual Learning. We aim to build systems that excel in performance but prioritize robustness, fairness, and privacy, critical requirements when dealing with time-series data. More info about the TRUMAN project can be found here.

PhD Position in Real-Time Multimodal Construction Transport Optimization & AI-Driven Decision Support (MULTIPLIER Project)

We are looking for a highly motivated and curious PhD candidate to contribute to the next generation of real-time, AI-driven decision support for multimodal construction logistics. The successful candidate will join the Industrial Engineering and Management Sciences (IEMS) section of the High-tech Business & Entrepreneurship (HBE) department at the University of Twente and will be in collaboration with Pervasive Systems Research Group at EEMCS Faculty.

This PhD position is part of the MULTIPLIER project, a large cross-disciplinary initiative addressing one of the key challenges of the Dutch construction transition: scaling prefabricated modular housing while drastically reducing logistics-related emissions.

With the ambition to deliver up to 50,000 prefab houses per year, modular construction creates large, predictable transport flows. MULTIPLIER leverages this as a demand-side trigger, while inland waterway transport (IWT) serves as the supply-side multiplier, offering scalable capacity and significant emission reduction.

The project focuses on end-to-end planning, connecting prefab factory production schedules with urban construction projects through an AI-driven decision-support system that synchronises production planning, IWT capacity, terminal availability, and urban delivery constraints.

The consortium includes University of Twente, Hogeschool Utrecht, prefab housing factories Plegt-Vos and HDO Groep, IT solution providers (Cape Groep, Bureau Voorlichting Binnenvaart), logistics and terminal partners (Peterson Nederland, Port of Twente, Metropolitan Hub System), municipalities (Amsterdam, Leiden, Den Haag), , Logistics Overijssel and Logistics Navigators. The academic supervision team includes Dr. Engin Topan and Dr. Rob Benthuis.

MULTIPLIER integrates operational data, optimization, and hybrid AI into a decision-support environment that advises when inland shipping is feasible, and how to bundle, schedule, and synchronise multimodal prefab logistics in zero-emission contexts. This requires an end-to-end planning.

In this PhD project, you will:

• Develop real-time optimization and hybrid AI models for end-to-end multimodal transport planning under uncertainty.
• Design synchronization, consolidation, and matchmaking algorithms that align prefab factory production schedules with IWT capacity, terminals, and urban delivery windows.
• Model multi-level planning decisions, connecting early feasibility assessment and quotation with tactical release planning and real-time operational dispatching.
• Incorporate urban and site-specific constraints, such as quay availability, zero-emission zones, access windows, and reverse logistics flows.
• Build explainable, scenario-based decision support that helps planners decide when and how IWT can be applied effectively.
• Work closely with a large multi-actor consortium, including factories, logistics providers, ports, and municipalities.
• Contribute to Living Lab pilots that demonstrate real-world applicability and scalable logistics impact.

We expect the PhD candidate to deliver the main deliverables to the consortium before 31 December 2028. In the remaining time of this position, the PhD candidate can write and finalize the thesis.

Project Controller EEMCS

Als Project Controller ben je een essentiële schakel tussen projectleiders, onderzoekers, financiële administratie en externe partners. Je ondersteunt projectleiders uit de wetenschappelijke vakgroepen van de faculteit bij de financiële beheersing van hun (inter)nationale, extern gefinancierde projecten. Je bewaakt de voortgang, richt de administratie helder en efficiënt in, werkt prognoses bij, en zorgt dat rapportages en verantwoordingen tijdig en juist worden opgeleverd. Hierbij werk je vanuit een onafhankelijke positie en geef je projectleiders advies dat hen helpt operationele en financiële beslissingen te nemen.

Tot jouw taken en verantwoordelijkheden behoren:

• het bewaken van de financiële voortgang van diverse extern gefinancierde (inter)nationale projecten.
• het opstellen van financiële verantwoordingen volgens de geldende subsidie- en projectvoorwaarden inclusief voorbereiding en begeleiding van accountantscontroles.
• het maken van begrotingswijzigingen, analyses en prognoses, inclusief het verzorgen van facturatie en rapportages.
• het proactief signaleren van risico’s, afwijkingen en kansen, zodat projectleiders hun projecten optimaal kunnen beheersen.

Als financieel aanspreekpunt ondersteun je projectleiders bij uiteenlopende vraagstukken en zorg je voor heldere uitleg en begeleiding. Je maakt deel uit van een gedreven en deskundig team van Project Controllers en werkt nauw samen met collega’s binnen UT-brede projectteams. Met jouw ondernemende houding draag je bij aan procesverbeteringen en verdere professionalisering binnen de afdeling en de faculteit.