דרושים Master Memory Rtl Automation Intern M/w/d ב-אינטל ב-Germany, Munich

This position is available as full time or part time.

Your area of responsibility:
We offer a Master student position for developing RTL automation environments to help our products to choose the right embedded memory strategy for fast and accurate integration.

The responsibilities will include but are not limited to:

• Ideate, plan, develop and run automated RTL test environments
• Compare and rate test coverage and test results
• Define test strategies to increase RTL code quality based on latest AI technology and algorithms
• Present results and best solutions to the internal memory team

• An excellent background in RTL (preferably system verilog and UVM)
• AI and test strategy experience

Education and Experience

• Degree (BS) in electrical engineering, software engineering, mathematics or equivalent experience
• Proven experience in HDL language
• experience in AI and software development
• Know-how on modern Software tools like python, perl, java, vscode
• Pursuing highest quality standards
• Excellent communication, fluent in English
• Demonstrated proficient documentations skills
• Must be able to work on multiple tasks concurrently.

Experience with Windows 11, Office 365 and UNIX/LINUX systems is required

In this role:

You will contribute to the success of the software projects running in the lab.

You will be building and implementing tests and demos involving the latest and upcoming hardware and software stacks.

You will be participating in the configuration, testing, analyzing, tuning, and debugging of software and hardware components for maximum performance.

You will also take responsibility in profiling and benchmarking CV and AI components and solutions on different hardware configurations.

With sufficient skills, experience, and aspiration you will be able to participate in new software development projects.

Hardware skills:

• Personal Computer and/or Server building experience
• PC hardware handing
• Firmware flashing
• Computer hardware debugging experience

Software skills:

• Installation and customizing OS to hardware needs (Linux or Windows)
• Knowledge about software, drivers, and firmware handling.
• Shell scripting
• Good understanding of computer networks and network protocols
• Basic knowledge of Databases
• Basic understanding of Active directory domain services
• Basic familiarity with Web stacks, MEAN or LAMP
• Good verbal and written English skills

A strong plus will be working experience with:

• Docker or other containerization technologies
• Python or C/C++ programing
• Experience in AI and GenAI software development- Remote access
• KVM, remote access protocols
• Industrial controls knowledge
• Experience in OpenCL, CUDA or other GPU programming languages and methodologies
• Working experience with software revision systems like git or subversion

This role requires good verbal and written communication skills in English.

The position involves working in an IT lab environment; the ability to move equipment around is necessary.

Key Responsibilities:

• Conduct research and a literature review on static data layout optimization techniques and their impact on program performance.
• Collaborate with compiler engineers to develop a prototype or extend existing LLVM passes for global static data reordering based on CPU performance events.
• Analyze the performance impact of data reordering on various software benchmarks and real-world applications.
• Collaborate with domain experts and supervisors to refine approaches and validate results.
• Document research findings, implementation details, and results comprehensively for thesis submission and possible publication.

Requirements:

• Academic Background: Enrolled in a Bachelor's or Master's program in Computer Science, Software Engineering, or a related field.
• Knowledge of LLVM: Prior experience with LLVM or similar compiler frameworks is highly desirable.
• Understanding of Compiler Concepts: Solid grasp of data layout strategies and optimization techniques.
• Analytical Skills: Strong problem-solving abilities with a focus on performance analysis and optimization.
• Communication: Good written and verbal communication skills for documenting and presenting findings.

Position Overview

We offer a Master student position to develop RTL automation environments, aiding our products in selecting the optimal embedded memory strategy for fast and accurate integration. Responsibilities include:

• Ideating, planning, developing, and running automated RTL test environments.

• Comparing and rating test coverage and results.

• Defining test strategies to enhance RTL code quality using AI technology and algorithms.

• Presenting results and solutions to the internal memory team.

For more information, visit .

Skills and Experience

The position requires the following qualifications:

• RTL Expertise : An excellent background in RTL, preferably with System Verilog and UVM.

• AI and Test Strategy : Experience in AI and test strategy development.

• Education : A degree (BS) in electrical engineering, software engineering, mathematics, or equivalent experience.

• HDL Language : Proven experience in HDL language.

• Software Development : Experience in AI and software development.

• Modern Software Tools : Know-how on modern software tools like Python, Perl, Java, and VSCode.

• Quality Standards : Pursuing the highest quality standards.

• Communication Skills : Excellent communication skills, fluent in English.

• Documentation Skills : Demonstrated proficient documentation skills.

• Multitasking : Must be able to work on multiple tasks concurrently.

• Operating Systems : Experience with Windows 11, Office 365, and UNIX/Linux systems is required.

With the increasing operating frequencies and miniaturization of electronics, transistors are densely packed, raising power densities and leading to transient self-heating effects. These effects alter device characteristics and impact circuit operation. Accurate modeling of self-heating requires precise characterization data, which is challenging to obtain due to the short time scales involved in advanced transistor devices like 3D FinFETs and Gate All Around FETs.

The goal of this thesis is to compare different characterization methods for analyzing the self-heating effect in advanced FinFET technologies and prepare these methods for Gate All Around technologies. This involves using both modeling approaches and experimental methods, including techniques like the AC-DC mixed method or pulsed measurements.

• Educational Background : ES, RFE