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Early career researchers as stakeholders in university decision‐making in Europe: comparative perspectives
(2026-01-07) Leišytė, Liudvika; Načinović Braje, Ivana; Almog, Shulamit; Baysan, Sultan; Carvalho, Teresa; Daunoraitė, Dovilė; Diogo, Sara; Papaioannou, Panourgias; Farmaki, Anna; Feldman, Shlomit; Külcür, Rakibe; Matijošytė, Inga; Pralgauskaitė, Sandra; Rangelova, Vanya; Šatkovskienė, Dalia
The voices of academics have traditionally been strong in university decision‐making bodies, where they participated in the shared governance of the university. It has been customary for senior academics to be represented in managing bodies and to exercise control over the key areas of strategy, finance, quality assurance, study programs, and/or human resources. With the new public management reforms that have swept through higher education (HE) systems, the power of academics has been reduced, while managerial guidance has increased, alongside the fostering of universities’ institutional autonomy. At the same time, the power of other stakeholders, such as students or industry representatives, has also been increasing as part and parcel of the governance reforms, albeit to different degrees and at different paces across various HE systems. In this context, this article seeks to examine the role that early career researchers (ECRs) play in university decision‐making bodies across different countries as internal stakeholders. The research is based on seven case studies from seven European and East Mediterranean countries drawing on documentary data and 55 semi‐structured interviews with ECRs and 14 managers, carried out in 2023–2024. Following stakeholder categories distinguished on the basis of their legitimacy, urgency, and power, this article investigates the extent to which ECRs perceive their voices to be heard. The findings show variance between the case studies regarding formal representation, with most universities in the study having limited representation of ECRs in university and faculty/school‐level decision‐making bodies. The voices of ECRs, however, are heard in informal ways.
Intersectionality at German universities: empowering teaching staff as change agents with higher education didactic workshops
(2026-01-07) Mergner, Julia; Pekşen, Sude; Leišytė, Liudvika
The increasing diversity at German universities has been accompanied by the demand to widen participation among all groups of students. This challenges higher education teaching, requiring learning environments that acknowledge diverse experiences and needs. While diversity‐sensitive approaches have been the dominant response, they often address single diversity dimensions in isolation, neglecting intersectional interdependencies and structural power relations. An intersectional perspective, however, shifts the focus to power dynamics, knowledge production, and inclusive educational practices. This article argues that such an approach has a good potential to enable lecturers and students to become change agents by fostering critical thinking, reflective agency, and ethical commitment to dismantling systemic inequalities. This is particularly challenging in the German higher education system, where critical, antidiscriminatory pedagogical perspectives are mostly limited to certain disciplines. At the same time, the teaching staff enjoy extensive teaching autonomy, which provides them with freedom for individual engagement in this area. Therefore, implementing intersectional approaches in teaching requires targeted educational interventions that support teaching staff. Building on the concept of intersectional pedagogy, we introduce a case study of a higher education didactic workshop that was designed to raise awareness of intersectional perspectives in teaching. The findings highlight the potential of such workshops to influence teaching practices and promote the engagement of disciplinary teaching communities with intersectionality. This article concludes by discussing the implications for further developing workshop concepts and empowering teaching staff and students as agents of change within the German higher education system.
Cycle-consistent generative adversarial networks for damage evolution analysis in fiber-reinforced polymers based on synthetic damage states
(2024-06-03) Helwing, Ramon; Mrzljak, Selim; Hülsbusch, Daniel; Walther, Frank
Analyzing computed tomography (CT) scans is challenging and time-consuming due to their high complexity. Machine learning, particularly in the form of segmentation techniques, has emerged as the state-of-the-art approach for defect detection in parts and materials. However, the lack of pixel-accurate labeled training data remains a significant challenge. This paper presents a damage state transformation approach based on a cycle-consistent generative adversarial network (CycleGAN) using fatigue damage states of fiber-reinforced polymers. The generated synthetic data is visually almost indistinguishable from real data. Introduced damage can be determined by calculating the damage removed during the transformation from a high-damage state to a low-damage state. Using multiple transformation steps in detecting and distinguishing different damage states the effectiveness is demonstrated. In addition, the virtual addition of damage to undamaged specimens is investigated. The results show that certain damages exhibit chaotic generation across successive slices while maintaining semantic connections in specific regions across multiple slices. Overall, this research presents a valuable approach for improved self-supervised damage detection and characterization in CT scans, with potential applications in materials analysis and structural health monitoring.
Data-efficient surrogate modeling of thermodynamic equilibria using Sobolev training, data augmentation and adaptive sampling
(2024-07-08) Winz, Joschka; Engell, Sebastian
Modern thermodynamic models, such as the PC-SAFT equation of state, are very accurate but also computationally intensive, which limits their applicability to process design optimization, for example. Surrogate models, which can be evaluated quickly, can be used to approximate the thermodynamic equilibria. However, this requires many data points from the flash calculation routine. In this paper, we investigate three approaches to reduce the number of samples and thus the effort needed to train the surrogate models. First, Sobolev training is used, where the surrogate model is trained not only on the output values, but also on derivative information. Second, data augmentation along the tie lines in LLE systems is proposed to generate samples without additional flash calculations. Third, adaptive sampling is revisited with a novel quality criterion. It is shown that the combination of these techniques can be used to significantly reduce the number of samples required.
Amtliche Mitteilungen der Technischen Universität Dortmund Nr. 1/2026
(Technische Universität Dortmund, 2026-01-14)