PhD Position in Predictive Modeling of Defects in Thermoplastic Composites

As part of the Dutch National research program Aviation in Transition, the Production Technology group at the University of Twente has a PhD position on the simulation of a novel assembly technique for thermoplastic composites in a T-joint configuration.

Background and challenge
Thermoplastic composite materials are considered a prime candidate for the next generation of commercial aircraft. Compared to thermoset composites, these materials offer improved toughness, dust-free assembly through thermoplastic welding, and improved recycling opportunities. To date, the aircraft manufacturing industry mainly relies on autoclave consolidation for the manufacture of medium-sized to large components. However, because autoclave consolidation is costly and energy-intensive, the industry is pursuing alternative technologies. One promising alternative is vacuum-bag-only (VBO) consolidation. This method is applied to large preforms manufactured using automated fiber placement. Consolidation of the final structure takes place under vacuum in an oven or on heated tooling, eliminating the need for an autoclave.

The VBO process shows significant potential for the cost- and energy-efficient manufacturing of large structures, but still requires further development. A key challenge lies in the formation of defects, such as in-plane or out-of-plane waviness and shape distortions, which can arise during layup or consolidation. Currently, preventing these defects relies on time-consuming and expensive trial-and-error methods. To overcome this, we aim to develop advanced simulation tools that can predict defect formation early in the process, enabling more efficient process window development and tool design.

Job description
We are looking for a PhD candidate to strengthen our research team and develop predictive models for defect formation and shape distortions during the layup and VBO consolidation of thermoplastic composite structures. The role will involve designing and conducting experiments to identify, analyze, and quantify the relevant physical mechanisms, followed by the development of models to describe these, leading to process simulations models to predict and hence to prevent such defects from occurring in practice. Earlier work on defect formation during autoclave and press consolidation processes forms an excellent starting point for the task at hand. As part of the project, you will collaborate closely with other researchers and share your results with industrial partners, contributing to impactful advancements in the aerospace industry.

PhD position on stage-based design approaches for meaningful technologies supporting evolving health conditions and needs

THE CHALLENGE

This PhD research will address the health-related challenges individuals encounter at various stages throughout the progression of their health condition. Many health conditions typically involve multiple stages and levels, with each stage being highly interrelated and characterized by changing needs. Examples include, but are not limited to, the gestational stages of pregnancy and the progressive phases of dementia, both of which have medically defined stages. Additionally, some stages may be self-defined, reflecting the diverse fluctuations and interruptions experienced by individuals over time, mostly beyond medical conceptualizations (e.g., emotional, social). Each stage and the transition points between different stages present unique challenges not only to the individuals moving through those stages, but also to other stakeholders who monitor and care for these individuals.

Capturing the stage-based health needs is essential to design more meaningful and tailored health technologies. This also requires novel perspectives and tailored considerations in the development of design methods and tools to explore idiosyncratic dynamics and needs in multiple stages observed in diverse health conditions. This PhD position focuses on the intersection of the three foci: understanding the stage-based needs, developing stage-based research approaches, and designing technologies for the stage-based needs.

Moreover, in addition to investigating current needs, projecting future staged-conditions and the role of meaningful technologies and data in addressing these stages are important. This involves not only understanding present experiences, but also anticipating how these stages might evolve over time, by employing futuring techniques and forward-thinking approaches. The PhD researcher can develop scenarios and strategies to be prepared for and address the needs of individuals and stakeholders as they navigate various stages of health conditions. Also, they can provide individuals and stakeholders with futuring tools and strategies to help them predict and reflect on their own future needs related to staged-based conditions.

JOB DESCRIPTION
You will explore and further the use of design methods and/or emerging technologies to tackle changing needs in diverse health conditions due to different stages, transitions, and disruptions.

You will reflect and be critical on the medically vs. personally defined; anticipated vs. unexpected; permanent vs. temporary stage-based health conditions; as well as their multifaceted relations with social, cultural, physical, and temporal contexts. You will hereby provide a more holistic and richer representation of the stages and transitions in relation to people’s daily lives, while informing the design space.

You will engage in transdisciplinary collaboration, while adapting a critical approach. Transdisciplinary collaboration involves integrating knowledge and experiences of non-academic stakeholders, beyond diverse disciplines. In this case, transdisciplinarity involves the perspectives of patients, caregivers, medical professionals, relevant communities and associations.

Contract Manager Mechanical Engineering

Universiteit Twente is op zoek naar een gedreven Contractmanager Werktuigbouwkunde. In deze rol ben jij de schakel tussen onze organisatie en onze contractanten en zorg je ervoor dat alle betrokkenen het beste uit zichzelf en de samenwerking halen.

Wat ga je doen?
Als Contractmanager Werktuigbouwkunde ben jij verantwoordelijk (operationeel, technisch en financieel) voor het beheren en optimaliseren van contracten met onze werktuigbouwkundige partners. Je stuurt de contractanten aan, motiveert hen en zorgt ervoor dat de prestaties voldoen aan onze hoge standaarden. Dit doe je door:

• Coördinatie en Communicatie: Het faciliteren van een vloeiende samenwerking tussen interne teams en externe partners.
• Prestatiemanagement: Het monitoren, beoordelen en evalueren van prijsopgaven/facturen en de prestaties van de contractanten, en waar nodig, bijsturen voor optimale resultaten.
• Technische Expertise: als sparringpartner op te treden als het gaat om technische uitdagingen. Met jouw kennis van werktuigbouwkundige installaties, om zo de kwaliteit en veiligheid te waarborgen.
• Innovatie en Verbetering: Het beoordelen van verbeteringsprocessen en het aanmoedigen van innovatie.

Coördinator Copyright Informatie Punt

Binnen het universitair onderwijs spelen auteursrechten een steeds belangrijkere rol. Docenten gebruiken auteursrechtelijk beschermd materiaal in het onderwijs. Het is daarbij belangrijk om te weten wat er mag en niet mag. Om docenten hierin te begeleiden start de Universiteit Twente het Copyright Informatiepunt (CIP) dat zal gaan fungeren als vraagbaak op het gebied van auteursrechten in het onderwijs.

Als coördinator CIP ga jij aan de slag met de oprichting van dit informatiepunt. Je maakt hierin gebruik van een zowel intern als landelijk netwerk. Je ondersteunt faculteiten in het doorontwikkelen van de huidige werkprocessen voor het juiste (her)gebruik van auteursrechtelijk beschermd materiaal in het onderwijs en zorgt dat de Easy Access regeling bewaakt wordt.

Als coördinator CIP ondersteun je de vijf faculteiten in het gebruik van de CopyrightCheck tool en werk je samen aan de ontwikkeling van relevante rapportages om aan de geldende verplichtingen te voldoen. Je vertegenwoordigt de Universiteit Twente bij nationale bijeenkomsten (UNL, SURF en UKB) op dit onderwerp en houdt proactief ontwikkelingen in het veld bij.

Je bent in deze functie zowel organisatorisch als inhoudelijk betrokken en je bent verantwoordelijk om dit onderwerp een impuls te geven binnen de universiteit.

PhD position on Neuro-symobolic AI for microfabricated fluidic sensors

The Pervasive Systems research group at the University of Twente is looking for a PhD candidate to perform research and development on a multidisciplinary project involving Neuro-symobolic AI and microfabricated fluidic sensors.

The main research objectives are:

• Conduct research in neuro-symbolic AI for microfabricated fluidic sensors, including but not limited to designing and implementing lightweight but accurate algorithms and models, conducting experiments, analyzing data, and interpreting results.
• Collaborate with the team to develop and optimize microfabrication processes for the sensors.
• Develop and test new sensor designs and configurations, and evaluate their performance.
• Write technical reports and research papers for publication in top-tier journals and conferences (Percom, Ubicomp, IJCAI, AAAI, NIPS, ICML).

The prospective candidates are expected to perform high-quality and internationally visible research that gets published at top-tier conferences and journals. Candidates will work at the Pervasive Systems Research group, Department of Computer Science, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente in the Netherlands. The candidates are expected to collaborate with project partners including the Integrated Devices and Systems (IDS) group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), at the University of Twente in the Netherlands.

The project
While a new generation of integrated sensor systems has been developed fast last years, it is now a necessity to explore recent work in symbolic Artificial Intelligence (AI) to overcome these limitations and allow for real-time fluid data processing by using a combination of deep neural networks and physics in flow sensing. By embedding human knowledge of physical quantities into deep neural networks, AI would be able to learn quicker and better how to use the sensing structures on the chip to give a physically relevant output signal. Using deep symbolic AI on microfluidic sensor data is therefore expected to cause a breakthrough in the easy design and use of cutting-edge multiparameter sensing systems. Therefore, the goal of this project is the realization of a demonstrator system containing multiple sensing structures together with a trained neural network, which outperforms the state-of-the-art multiparameter systems, for real-time quality control of products made in chemical or pharmaceutical micro reactors, or in the food industry. Sub-goals include the generation of novel deep symbolic AI that can combine traditional physics rules with modern deep learning techniques to effectively deal with raw sensor data, verify physical constraints, understand the complex physical effects in microfabricated fluid channels, and improve future chip designs based on existing and novel (hidden) causal relations found through AI model interpretability.

The vacancies are within the scope of the MOSAIC - enhancement of MicrOfluidic Sensing with Artificial IntelligenCe project, and is a collaboration between famous research groups of University of Twente and industrial companies. The project is funded by the national research foundation NWO, together with supporting partners from the industry.