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Reach new heights.

Siemens Energy Graduate Program

If you’re ready to get hands-on experience working on solutions that directly impact climate change and drive the energy transformation, then the Siemens Energy Graduate Program is for you. The Siemens Energy Graduate Program is a two-year experience for recent master’s and doctoral graduates that includes three eight-month assignments designed to help you develop and strengthen your knowledge and skills while building a successful career with Siemens Energy. You’ll get the support you need to leverage innovative technologies, gain access to extensive energy experience, and help achieve an ambitious strategy to drive the energy transition.

When you join our Graduate Program, you become a part of Siemens Energy. You get to share our purpose in energizing society by supporting customers with transitioning to a more sustainable world. You will work on real global issues and help create solutions that directly make an impact.

We are looking for talented and ambitious recent graduates from all backgrounds who will help us shape the future of energy and our company. Here, you’ll build your career with one of the world’s leading organizations in the energy technology industry, and work with a team that shares an uncompromised passion to make a global impact. The next wave will start in September 2024. We will start accepting applications in mid November/December 2023.

As part of the Siemens Energy team, you’ll be contributing to one of the biggest challenges in the world. Our program will help you focus your human energy, create solutions with your colleagues and work with experts across the globe to energize society. You have spent a lot of time studying–now is the time to get the support needed to make an impact.

Hear what our Graduate Program alumni have to say about their experience.   

Here are a few highlights about the program: 

• You will work on three challenging projects (each project is eight months for a total of 24 months / two years).
• You’ll be actively integrated into the day-to-day business of your project teams, assuming full responsibility for the tasks assigned to you.
• You will be assigned a mentor and shadow a role or position you are interested in.
• You will network with peers and business leaders and take part in the graduate training modules with access to tools to help you grow.

Looking for additional information about the Siemens Energy Graduate Program? Here are some of our most frequently asked questions. 

What types of candidates are we looking for?

We're looking for ambitious master’s and doctoral graduates or equivalent (up to one year ago and relevant to the role) seeking the opportunity to engage in challenging projects that contribute to making sustainable, reliable and affordable energy possible now, and for the future.

As an ideal applicant, you are driven, motivated, open-minded and focused on delivering results. You are also proactive toward owning important projects and driving your own development. You have the courage and honesty to challenge yourself, embrace your strengths, and build upon them.

A minimum of three months of international experience achieved through studying and/or working is required.

Additionally, significant practical work experience up to 36 months through internships, working student activities, or employment is required. 

Candidates must have excellent English acumen to work in a multi-cultural environment and be flexible and mobile during the program.

What is the typical recruitment/interview process?

Once you have submitted your application, the Siemens Energy Talent Acquisition team will review resumes and candidate qualifications. If your resume seems to be a good match for our Graduate Program, you will be invited to take an online assessment. Upon successful and qualified completion of the assessment, you will be invited to an “on demand video interview” where you record and submit the answers to our questions. The last step in our process will be a case study followed by an interview. Siemens Energy will guide you along all steps of the process.

When should I apply?

We have opportunities across the globe and will start accepting applications mid November/December 2023. Stay tuned!

What is the role of the mentor?

Your mentor plays an important role in the Siemens Energy Graduate Program as they support and guide you throughout the experience. Together, you will decide which assignments you’ll support depending on your interest, personal development, and business need. Additionally, your mentor will provide advice and feedback and support your overall developemtnatl activities.

What countries offer the Siemens Energy Graduate Program?

The Siemens Energy Graduate Program is a global program which means opportunities are available in several countries around the world. We accept applications from all countries, from candidates of all nations because we believe that diversity of thought comes from all over.

What is my employee status as a member of the Siemens Energy Graduate Program?

Being part of the Siemens Energy Graduate Program means you’re a Siemens Energy employee with a regular permanent employment contract. The conditions of this contract depend on local requirements and regulations. The Graduate Program is not an internship program.

Is the assignment abroad mandatory?

Yes, but we need to be flexible based on country-pandemic restrictions and visa regulations. As a global company, the assignment abroad will give you more exposure to our culture. We’ll support you in selecting the best destination for your assignment abroad so you can get the most out of it. Due to Covid circumstances and regulations, we may need to adapt and find adaptive solutions.

Can I choose the area I will be working in?

Yes, we encourage you to apply to the specific job position you are interested in. We try to match your interests and qualifications to the demands coming from the business. Siemens Energy offers job positions in the the following areas: Assurance, business development/sales management, cybersecurity, digitalization, engineering, environmental management, finance, industrial/mechanical/electrical/software engineering, IT, marketing, operations, production and manufacturing, performance controlling and business development, product life cycle management, sales, and project management. Click here to see all opportunities.

What kind of role could I expect after the program?

This depends on your performance and interests during the program. You’ll discuss this with your manager and mentor throughout and they will support you in your transition once the program comes to an end. You may be offered a position in your home organization, home country or abroad, based on availability.

Will I get to meet and work with peers?

Every graduate is paired with an alumni who will share their lessons learned and be a resource during the program. Your alumni buddy will introduce you to their network and other alumni within Siemens Energy and encourage you to build a strong global network, which is one of the key factors of the Graduate Program.

What distinguishes a Siemens Energy Graduate Program (SEGP) participant from a Siemens Energy employee?

As a SEGP participant you considered an employee of Siemens Energy with an unlimited contract and all rights and responsibilities of a SE employee. In your first 2 years you are a graduate and go through 3 different assignments of 8 months each including one international assignment, participate in different development modules, trainings etc. and are part of a very intensive global network. You are also assigned a mentor who may offer shadowing days, as well as an alumni buddy who can share her/his experiences with the program.

What is the purpose of SEGP?

The three goals of the Siemens Energy Graduate Program are:

• Get business insights
• Be supported to unleash your Human Energy
• Be part of a global Network

Apply now to our Siemens Energy Graduate Program

Climate change is not waiting, neither should you! If you are passionate about shaping the energy of tomorrow, then the Siemens Energy Graduate Program is for you.

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Financial Analysis of Siemens Group – Undral Munkh-Erdene

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Undral Munkh-Erdene

Master's thesis, financial analysis of siemens group, thesis defence.

• Supervisor: Ing. Bc. Jana Hvozdenská
• Reader: Ing. Oleksandra Lemeshko

Citation record

Iso 690-compliant citation record:, full text of thesis, contents of on-line thesis archive, other ways of accessing the text, masaryk university.

Master programme / field: Finance and Accounting / Finance (Eng.)

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• Chair of Integrated Systems
• TUM School of Computation, Information and Technology
• Technical University of Munich

Master Theses

Available topics.

Interested in an internship or a thesis?   Often, new topics are in preparation for being advertised, which are not yet listed here. Sometimes there is also the possibility to define a topic matching your specific interests. Therefore, do not hesitate to contact our scientific staff, if you are interested in contributing to our work. If you have further questions concerning a thesis at the institute please contact  Dr. Thomas Wild .

MA : Hardware-Aware Layer Fusion of Deep Neural Networks

• Open thesis as PDF.

Hardware-Aware Layer Fusion of Deep Neural Networks

Description.

Dataflow and mapping of Convolutional Neural Networks (CNN) influences their compute and energy efficiency on edge accelerators. Layer fusion is a concept which enables the processing of multiple CNN layers without resorting to costly off-chip memory accesses. In order to optimally implement layer fusion, different combinations of mapping and scheduling parameters need to be explored. We, at the BMW group, offer you a challenging master thesis position that aims to optimize the fusion strategy of a given CNN workload for maximal data reuse and resource utilization.

Prerequisites

• Strong knowledge in computer vision concepts, and convolutional neural networks.
• Hands-on experience with Xilinx FPGAs, Verilog/VHDL/HLS.
• Excellent programming skills in  C, Python. Experience in Tensorflow 2, Git, Docker is a plus.
• Highly motivated and eager to collaborate in a team.
• Ability to speak and write in English fluently.

[email protected]

Supervisor:

Assigned topics, ma : hardware-accelerated linux kernel tracing, hardware-accelerated linux kernel tracing.

Tracing events with hardware components is one powerful tool to monitor, debug, and improve existing designs. Through this approach, detailed insights can be acquired, and peak performance can be achieved, while being a challenging task to be integrated with good performance. One of the major challenges of tracing is to collect as much information as possible with ideally no impact on the to-be-analyzed system. Herewith, it can be ensured that the gained insights are representative of an execution without any tracing enabled. In this work, a hardware tracing component should be leveraged to reduce the intrusiveness of existing software tracing mechanisms in the Linux kernel. 

This should be integrated and tested on a hardware platform based on a Xilinx Zynq board. This features a heterogeneous ARM multicore setup directly integrated into the ASIC, combined with programmable logic in the FPGA part of the chip. In the FPGA a hardware accelerator is already implemented that should be traced with the new component.

To successfully complete this work, you should have:

• experience with microcontroller programming,
• basic knowledge about Git,
• first experience with the Linux environment.

The student is expected to be highly motivated and independent.

MA : Multicore-Optimierung eines bildverarbeitenden Systems

Multicore-optimierung eines bildverarbeitenden systems.

Im industriellen Umfeld werden Informationen zunehmend in visuellen Codes (z.B. Strichcodes, QR-Codes) zur automatisierten Verarbeitung abgelegt. Steigende Durchsatzzahlen stellen immer höhere Anforderungen an die Geschwindigkeit der Datenverarbeitung. In dieser Arbeit soll anhand eines kostengünstigen kommerziell erhältlichen Multicore- Systems untersucht werden, inwieweit bisher durch Hardware realisierte Verarbeitungsgeschwindigkeiten durch Parallelisierung der Auswertungsschritte in CPU-Systemen erreicht, werden können. Insbesondere soll untersucht werden, ob spezialisierte Co-Prozessoren (z. B. Vector Processing Units (VPUs)) zur Beschleunigung beitragen können oder wie diese auf die Aufgabe hin optimiert gestaltet werden können (Application-Specific Instructionset Processor (ASIP)).

MA : A Deep Dive into C-States, Idle Governors and the Prospects of an eBPF Idle Governor

A deep dive into c-states, idle governors and the prospects of an ebpf idle governor.

Linux is one of the most utilized Operating Systems in Embedded Systems and Cloud Infrastructure worldwide. Sustainability will become more relevant in the future and saving power is a crucial aspect. This shows the increasing importance of efficient Linux Power Management.

The Power Management in Linux is implemented in several kernel subsystems correlating to hardware characteristics, like P-States (Frequency Scaling) and C-States (Sleep States). This thesis examines the Idle Power Management of Linux, and therefore focuses on C-States. C-States are per Core states and allow parts of the core to shut down individual features. Each processor implements C-States in different ways. Increasing C-State number, e.g. C6, translate to a deeper sleep with lower energy consumption and higher power-on reaction time.

The recently released eBPF functionality makes the kernel more programmable, bypassing the original monolithic characteristics. This mechanism can be divided into four components: the eBPF hooks in the kernel, the interfaces, the in-kernel eBPF infrastructure to execute eBPF bytecode and compile into native code and verify the code and finally the eBPF application itself, which can be written in a C like dialect and compiled into eBPF bytecode by LLVM and GCC.

This thesis aims to analyze and compare the idle governors in the current Kernel in specific situations. It also should provide insight in the C-State usage depending on the architecture. The data is acquired using specific Tracepoints within the Kernel, which can be recorded and parsed with the Kernel Tool perf. Furthermore, we explore the feasibility of a custom eBPF powered idle governor.

MA : Design and Implementation of a Memory Prefetching Mechanism on an FPGA Prototype

Design and implementation of a memory prefetching mechanism on an fpga prototype.

Their main advantages are an easy design with only 1 Transistor per Bit and a high memory density make DRAM omnipresend in most computer architectures. However, DRAM accesses are rather slow and require a dedicated DRAM controller that coordinates the read and write accesses to the DRAM as well as the refresh cycles. In order to reduce the DRAM access latency, memory prefetching is a common technique to access data prior to their actual usage. However, this requires sophisticated prediction algorithms in order to prefetch the right data at the right time. The Goal of this thesis is to design and implement a DAM preloading mechanism in an existing FPGA based prototype platform and to evaluate the design appropriately. Towards this goal, you'll complete the following tasks: 1. Understanding the existing Memory Access mechanism 2. VHDL implementation of the preloading functionalities 3. Write and execute small baremetal test programs 4. Analyse and discuss the performance results

• Good Knowledge about MPSoCs
• Good VHDL skills
• Good C programming skills
• High motivation
• Self-responsible workstyle

Oliver Lenke

[email protected]

MA : SmartNIC Enhancements for Network Node Resilience

Smartnic enhancements for network node resilience.

The Chair of Integrated Systems participates in the DFG Priority Program “Resilient Connected Worlds” by the German Research Foundation (SPP 2378). Our goal is to investigate which resilience functions, that conventionally are provisioned by the central compute resources of Internet Networking or Compute Nodes, can meaningfully be migrated onto the Network Interface Card (NIC). By inspecting packet streams at full line rate (10 – 40 Gbps) a set of resilience functions, such as access shields against a known set of traffic flows or redundant flow processing for a selected and configured number of flows, shall be offloaded from centralized compute resources and offered in a more performant and energy-efficient manner. Flows are identified by their so-called 5-tuple consisting of source-/destination IP addresses and transport protocol ports as well as the protocol field of the IP packet header. During the Bachelor/Master Thesis, you will develop VHDL code for realizing one or more of the SmartNIC Resilience building blocks: 5 tuple address matching against a preconfigured set of addresses, perform the packet duplication for delivery to different processor cores or threads, investigate methods to flexibly perform the address match on the entire or a variable subsection of the 5 tuple array.

• VHDL coding, synthesis and FPGA prototyping
• Braodband communication or Internet Networking Technologies, in particular OSI Layer packet header formats
• Digital circuit design

Marco Liess Room N2139 Tel. 089 289 23873 [email protected]

MA : Parsimonious Semantic Segmentation Training Using Active Learning and Synthetic Data

Parsimonious semantic segmentation training using active learning and synthetic data.

The goal of this thesis is to implement an augmentation pipeline for both runtime accuracy improvement and training time generalization. At training time the augmented examples add diversity to the dataset, while at runtime the augmentation injects more information in addition to the RGB color channels, to help the CNN detect semantic segmentation features. The thesis will also explore different loss formulas and loss learning to make training semantic segmentation easier with fewer labeled examples. Finally, the CNN will be pruned and quantized for faster execution, while the rest of the processing (pre, post) pipeline will be accelerated on GPU.

To successfully complete this project, you should have the following skills and experiences:

• Good programming skills in Python and Tensorflow
• Good knowledge of neural network training theory
• Experience with convolutional neural networks for semantic segementation

The student is expected to be highly motivated.

Nael Fasfous Department of Electrical and Computer Engineering Chair of Integrated Systems

Phone: +49.89.289.23858 Building: N1 (Theresienstr. 90) Room: N2116 Email: [email protected]

MA : Neural Style Transfer for Synthetic Data

Neural style transfer for synthetic data.

Neural networks have become the state-of-the-art in solving a variety of computer-vision problems, often outperforming classical image processing algorithms by a large margin. These applications range from autonomous vehicles to complex control of robots. However, training neural networks presents some difficulties. First and foremost is the cost of human effort to label and collect suitable training data (number and critical situations) in production environments for training purposes. Synthetic training data is one potential solution to this challenge.

In the context of this work, a neural network for the control of an automated production line should be trained using synthetic data. For this purpose the following milestones planned:

• Developement of a 3D-model for the generation of training data.
• Automatic synthesis of images and ground truth data to train the image processing algorithm.
• Adaptation of the synthetic training data to the real world (style transfer)
• Outperforming neural networks classically trained on limited amount of real data

Alexander Frickenstein Email: [email protected]

MA : Anomaly Detection and Active Learning for Semantic Segmentation Tasks

Anomaly detection and active learning for semantic segmentation tasks.

Clean, labeled datasets are an invaluable asset to research and industry for training and deploying machine learning algorithms such as convolutional neural networks (CNN). Procuring such datasets involves data collection, sorting and labeling, all of which are typically done by humans. This expensive process is time consuming, costly and does not scale well, even when outsourced. The field of anomaly detection and active learning aims to tackle these challenges. In active learning, a CNN can be trained on a small set of labeled data. Once deployed in a real-world scenario, an uncertainty or loss predictor can be implemented alongside the algorithm to predict which data would result in high loss for the model. These non-trivial examples can be collected actively during deployment and forwarded to humans or more complex algorithms to observe, label and retrain the deployed CNN on. In anomaly detection, a network can predict which samples represent outliers or interesting anomalies with respect to the rest of the dataset. This further helps humans clean and sort such examples accordingly.

The goal of this thesis is to implement an anomaly detector and an uncertainity head to a CNN-based semantic segmentation application. The implementation will be tested on a real-world industrial AI application.

MA : Learning to Prune and Quantize Transformers

Learning to prune and quantize transformers.

Advances in the deep learning architectures for computer vision applications have lead to new neural architectures such as vision transformers. These differentiate themselves from typical convolutional neural network-based implementations by decoupling the process of feature aggregation and transformation. Excellent performance is achieved through self-attention and self-supervision.

In this master thesis, visual transformers will be implemented in the first step. Following verification of state-of-the-art results, the transformers will be compressed through quantization and pruning to minimize their computational complexity on the inference hardware.

• Good knowledge of neural networks, basic knowledge of transformers

MA : Sparse Lookup Tables with dynamic precision adaptation for image processing on FPGA

Sparse lookup tables with dynamic precision adaptation for image processing on fpga.

In image processing, non-linear transfer functions, such as sigmoid- or logarithm-shaped functions, are being used for mapping the input into different domains. For dedicated FPGA implementation of general image processing pipelines, these transfer functions are usually implemented by LUTs (Lookup Tables). Although the LUT-based method is more concise than some approximate direct implementation, it consumes a lot of resources. To save FPGA resources, sparse LUTs can be used, but it is to be noticed that the matching accuracy is then approximated to a certain acceptable range.

To further reduce the resource consumption, while maintaining or even improving the output accuracy, we propose a dynamic loading mechanism. In order to make full use of the resources on the chip, instead of placing one sparse LUT on chip, two function-wise complemented memory blocks shall be implemented in the data path of the processing pipeline. One of the memory blocks shall be filled only with the data points that fit the local range of current input data stream. Another one works as a general ultra-sparse LUT to map the input data into the inaccurate global range. In summary, a permanent memory block of very sparse/inaccurate data points should be kept on FPGA, which is then complemented by a memory block of accurate data points which are dynamically swapped in and out from an off-chip memory (DRAM). Based on this proposal, we need to investigate a dynamic loading mechanism for that accurate memory block, such that the input will fall into the local range with rational high probability.

In this work, a prototype of a sparse LUT with a dynamic precision adaptation mechanism should be developed on FPGA. In this thesis, several questions should be answered:

• How does the architecture of the implementation look like?

• What memory configuration should be used?

• How to determine when to load new data for the accurate memory block?

• What is the trade-off between accuracy and resource consumption?

MA : Runtime Reconfigurable Winograd-based FPGA Accelerator for CNN Inference

Runtime reconfigurable winograd-based fpga accelerator for cnn inference.

Convolutional neural networks have proven their success in extracting features from images and producing predictions for different tasks such as classification, segmentation and object detection. However, the superior performance of modern deep neural networks can be mostly backtracked to high model complexity and extensive hardware requirements. In this research internship, the complexity of convolution is reduced by quantization and Winograd minimal filtering algorithms. The prediction quality is regulated using dynamic reconfigurable Winograd acceleration. 

• Good programming skills in C/C++
• Good knowledge of neural networks, particularly convolutional neural networks
• VHDL/Verilog or OpenCL would be encouraged. 

The student is expected to be highly motivated and independent. By completing this project, you will be able to:

• Understand the impact of quantization, Winograd convolution and task specific accuracy. 
• Implementation of run-time reconfigurable Winograd Convolution on FPGA using OpenCL. 
• Evaluate trade-offs between flexibility, prediction accuracy and resource consumption

Manoj Vemparala Autonomous Driving BMW AG

Email:   [email protected] 

Nael Fasfous

Department of Electrical and Computer Engineering Chair of Integrated Systems

Phone:  +49.89.289.23858 Building:  N1 (Theresienstr. 90) Room:   N2116 Email:   [email protected]

Daniel Guggenheim School of Aerospace Engineering

College of engineering, master's thesis defense: anna gulan.

Location URL

Master's Thesis Defense

(Advisor: Prof. Mavris)

"Conceptual, Trajectory-Based Structural Sizing Method for Hypersonic Glide Vehicles"

Monday, April 22

Collaborative Visualization Environment (CoVE)

Weber Space and Technology Building (SST II)

Microsoft Teams

In recent years, interest in hypersonic vehicles has rapidly developed resulting in an increase in hypersonic research and funding. This push is motivated by the hope that hypersonic vehicles will improve mission performance including velocity, range, and maneuverability. These vehicles are considered highly sensitive to weight though the specific relationship between weight and performance of hypersonic glide vehicles has not been well defined. The first research question will explore this relationship assessing the effects mass on performance parameters including terminal velocity, range, heat load, and mission time. The maximum heat load and terminal velocity were found to be the most sensitive to mass changes and when maneuvers are included in the flight path the total distance becomes increasingly more sensitive to mass.

During the conceptual design phase, engineers rely on weight estimations to ensure the vehicle will meet performance requirements. Current launch weight estimations typically rely on historical regressions. While useful, these regressions are outdated as they do not incorporate hypersonic vehicles and novel technology. This practice results in several gaps as the regressions lack background context reducing opportunities for pinpointing the driving loads and optimization. Additionally, they do not rely on the trajectory or performance parameters of the specific mission requirements. The second research question seeks to address these gaps by introducing a trajectory-based sizing tool to be utilized early in the design process. This tool will produce a more accurate initial weight estimation capable of identifying driving weight parameters. The results showed that for most cases the external pressure was the driving loading condition with the secondary driver being buckling due to bending moment. The peak load typically occurred in the terminal phase during the final dive. Additionally, the sensitivity of structural sizing by aerodynamic parameters was assessed. It was found that the initial height, terminal flight path angle, mission time, and range were the most significant contributors.

When traveling at hypersonic speeds, the load bearing structure will experience high temperatures. These temperatures are caused by both surface heat transfer and skin friction heating. This thermal energy heats the vehicle structure and causes material strength degradation. To reduce this effect a robust thermal protection system (TPS) is needed to shield the structure from the intense thermal energy. The TPS is typically sized to ensure the structural material does not heat beyond its maximum allowable temperature but could be increased in size to reduce the temperature felt by the structure. In attempt to explore the relationship between structural heating and strength degradation, the third research question will explore this relationship and the structural weight reduction that will occur from a reduced operating temperature. The model found that the structural weight increased exponentially with increasing temperature.

·         Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)

·         Dr. Adam Cox– School of Aerospace Engineering

·         Dr. Kenneth Decker – SpaceWorks Enterprise

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• Bachelor- and Master Theses

Master Thesis

If you are interested in writing your master thesis at the chair of Prof. von Siemens, please send the following documents to: petersen[at]econ.uni-frankfurt[dot]de

• Letter of motivation (including descriptions on potential research questions)
• Academic transcripts, i.e. QIS excerpt
• Curriculum vitae

After screening the required documents, we will contact you.

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ENGL 700 A: Master's Thesis

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