Currently, the labor market is facing many challenges after the Covid pandemic. While many employees were laid off during the pandemic, companies are now searching intensively for qualified workers. While the average vacancy rate is rising, job portals also report new open positions peaks. In this context, Gen Z becomes relevant, as they have already partially entered the labor market and expect to account for 27% of the global workforce in 2025. This generation is described as the most socially oriented and educated generation and wants to actively make a difference. Due to the labor shortage, there is increasing competition for highly qualified workers, which makes it particularly relevant for companies to understand what Gen Z expects from their employers. Therefore, this study investigates the meaning and assessment of Gen Z regarding the sustainability and purpose of a workplace.  To address this research gap and why Gen Z applies to a specific company, three focus groups with 5 participants each were conducted. The participants were master’s students enrolled in a business program. The comparison between the existing literature and the findings shows that the sustainability and purpose of Gen Z concerning the workplace are essential. Participants indicated that they would prefer working for a sustainable company. It is crucial to Gen Z in the study that their values align with the company's values and that there is a high level of transparency and authenticity. A limitation of the study's findings are more catered to Gen Z business master students and may not apply to the whole generation.

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With the advances of very large scale integration (VLSI) technology, the feature size has been shrinking steadily together with the increase in the design complexity of logic circuits. As a result, the efforts taken for designing, testing, and debugging digital systems have increased tremendously. Although the electronic design automation (EDA) algorithms have been studied extensively to accelerate such processes, some computational intensive applications still take long execution times. This is especially the case for testing and validation. In order tomeet the time-to-market constraints and also to come up with a bug-free design or product, the work presented in this dissertation studies the acceleration of EDA algorithms on Graphics Processing Units (GPUs). This dissertation concentrates on a subset of EDA algorithms related to testing and validation. In particular, within the area of testing, fault simulation, diagnostic simulation and reliability analysis are explored. We also investigated the approaches to parallelize state justification on GPUs, which is one of the most difficult problems in the validation area. Firstly, we present an efficient parallel fault simulator, FSimGP2, which exploits the high degree of parallelism supported by a state-of-the-art graphic processing unit (GPU) with the NVIDIA Compute Unified Device Architecture (CUDA). A novel three-dimensional parallel fault simulation technique is proposed to achieve extremely high computation efficiency on the GPU. The experimental results demonstrate a speedup of up to 4Ã compared to another GPU-based fault simulator. Then, another GPU based simulator is used to tackle an even more computation-intensive task, diagnostic fault simulation. The simulator is based on a two-stage framework which exploits high computation efficiency on the GPU. We introduce a fault pair based approach to alleviate the limited memory capacity on GPUs. Also, multi-fault-signature and dynamic load balancing techniques are introduced for the best usage of computing resources on-board. With continuously feature size scaling and advent of innovative nano-scale devices, the reliability analysis of the digital systems becomes more important nowadays. However, the computational cost to accurately analyze a large digital system is very high. We proposes an high performance reliability analysis tool on GPUs. To achieve highmemory bandwidth on GPUs, two algorithms for simulation scheduling and memory arrangement are proposed. Experimental results demonstrate that the parallel analysis tool is efficient, reliable and scalable. In the area of design validation, we investigate state justification. By employing the swarm intelligence and the power of parallelism on GPUs, we are able to efficiently find a trace that could help us reach the corner cases during the validation of a digital system. In summary, the work presented in this dissertation demonstrates that several applications in the area of digital design testing and validation can be successfully rearchitected to achieve maximal performance on GPUs and obtain significant speedups. The proposed algorithms based on GPU parallelism collectively aim to contribute to improving the performance of EDA tools in Computer aided design (CAD) community on GPUs and other many-core platforms. / Ph. D.