Semantische Suche

Ziel des Projekts "Effektive Security Awareness am KIT" ist es, eine erste Security Awareness Maßnahme speziell für KIT Mitarbeitende zu entwickeln, zu evaluieren und auf der Basis zu verbessern, um diese dann am KIT zu etablieren. Die beiden Themenkomplexe, die für dieses Vorhaben vorgesehen sind, sind einerseits eine allgemeine Sensibilisierung für Informationssicherheit (d.h. Beschäftigte verstehen, dass nur gemeinsam ein angemessenes Sicherheitsniveau erreicht werden kann, wer die Ansprechpartner sind, wo sie allgemeine Informationen wie die Security Policies finden) und andererseits das Thema Sicherer Arbeitsplatz und E-Mail-Sicherheit (inkl. der Erkennung und des Meldens von Phishing E-Mails und der Verwendung von S/MIME).

Aufgabenfelder: ▪ Durchführung von Literaturrecherchen ▪ Erstellung und Korrektur von Informationsmaterialien wie z.B. Grafiken, Texte, etc. ▪ Unterstützung bei der Konzeption, Implementierung und Durchführung von Nutzerstudien ▪ Eingabe und statistische Auswertung von Daten mit Hilfe von R Weitere Details können mit dem Ansprechpartner im Rahmen eines persönlichen Gesprächs erörtert wer-den.

Ihre Voraussetzungen: ▪ unabhängiges, eigenverantwortliches und strukturiertes Arbeiten ▪ Interesse sich in neue und sich stets fortentwickelnde Themen einzuarbeiten ▪ Grundkenntnisse bei grafischer Gestaltung (z.B. Photoshop) wünschenswert ▪ Grundkenntnisse der IT-Sicherheit wünschenswert, aber nicht zwingend erforderlich ▪ Grundkenntnisse in quantitativen und qualitativen Methoden wünschenswert ▪ Erfahrung mit Befragungssoftware (z.B. SosciSurvey) und Statistik-Software (z.B. R) von Vorteil ▪ gute Deutsch und Englischkenntnisse

Wir bieten: ▪ Abwechslungsreiche Arbeit mit starkem Bezug zum realen Nutzer ▪ Erfahrung im wissenschaftlichen Arbeiten

The research assistants will support the scientific staff of the Information Service Engineering (ISE) research group working on large open Knowledge Graphs in tasks like data analysis, knowledge modelling, data cleaning and knowledge mining.

Qualifications and Skills:

We are looking for highly motivated students (Bachelor or Master level) in computer science, industrial engineering, information sciences or similar, preferably at the Karlsruhe Institute of Technology ( KIT) <ul>

 <li> Knowledge and experience in the development of software applications, preferably in Python and/or Java </li>
 <li> Basic knowledge of Natural Language Processing (NLP), Linked Data Engineering, Information Extraction and/or Data Science </li>
 <li> Basic knowledge in the use of knowledge graphs, such as DBpedia or Wikidata </li>
 <li> Ability to quickly learn new topics and tasks </li>
 <li> Sufficient language skills in English to work in an international team </li>

</ul>

The payment is based on the rates of the state of Baden-Württemberg for academic assistants. The employment is temporary.

Information Service Engineering (ISE) at FIZ Karlsruhe investigates models and methods for efficient semantic indexing, aggregation, linking and retrieval of comprehensive heterogeneous and distributed data sources. To this end, both statistical and linguistic analysis methods (NLP) as well as machine learning in combination with symbolic logic are applied. ISE research relies and extends on knowledge representation standards developed for the Semantic Web. ISE research application areas include but are not limited to solutions for knowledge extraction, semantic annotation, semantic and exploratory search, as well as recommender systems and question answering. Besides basic methodological research, domains of applied ISE research are, amongst others, cultural heritage, digital humanities, materials science, and research data management. 

Background Wake turbulence are pairs of turbulence generated by aircraft while taking off (Figure 1, left). Flying into wake turbulence can destabilize an aircraft during landing and cause go-around. Thus, minimum distance between landing aircraft is prescribed (Figure 1, right). To make most use of airports’ capacity and ensure environmental sustainability, we will utilize deep neural network to detect wake turbulence.

What prerequisites do you need? • Solid programming skills (e.g. Python). • Strong foundation in machine learning, deep learning or artifitial intelligence. • Experience in LiDAR data is a plus.

What do we offer for you? • A contract with 20-80h per month (the salary depends on the university degree). The contract can last between 2 and 6 months. • Flexible working hours, also working at home possible.

Background

Wake turbulence are pairs of turbulence generated by aircraft while taking off. Flying into wake turbulence can destabilize an aircraft during landing and cause go-around. Thus, minimum distance between landing aircraft is prescribed. To make most use of airports’ capacity and ensure environmental sustainability, we will utilize deep neural network to detect wake turbulence.

What prerequisites do you need?

•Solid programming skills (e.g. Python).

•Strong foundation in machine learning, deep learning or artifitial intelligence.

•Experience in LiDAR data is a plus.

What do we offer for you?

•A contract with 20-80h per month (the salary depends on the university degree). The contract can last between 2 and 6 months.

•Flexible working hours, also working at home possible.

We look forward to your application!

The positions are linked to our project ONE4ALL (Horizon Europe 2022), which aims to boost manufacturing plants’ transformation, especially SMEs, towards industry 5.0 (I5.0), reinforcing their resilience under unexpected changes in social needs. Our part in this project is the digital replication of the physical modules and processes through data-driven digital twins and controlled by a self-learning AI-based distributed and multidisciplinary decision support system (DSS).

The tasks include: • Gathering data requirements and linking them to objectives • Working on developing an architecture for digital twins of manufacturing systems • Regular communication with project partners and demonstrators (manufacturing facilities) • Implementing a proof of concept for the above tasks • Documenting all tasks and outcomes

Qualifications and Skills: We are looking for highly motivated students (Bachelor or Master level) in computer science, industrial engineering, information sciences or similar, preferably at the Karlsruhe Institute of Technology (KIT) • Knowledge and experience in the development of software applications, preferably in Python and/or Java • Basic knowledge of Internet of Things (IoT) devices, Information Extraction and/or Data Science • Experience and knowledge in modeling, simulation, digital twins and manufacturing systems is a plus • Willingness and ability to communicate with project partners of different backgrounds • Sufficient language skills in English to work in an international team

We offer: • A contract with 20-80h per month (the salary depends on the university degree). The contract can last between 2 and 6 months. • Flexible working hours, also working at home possible. • A variety of tasks so that you can learn a lot. Also state-of-the-art research can be performed and will be published together with the student.

The payment is based on the rates of the state of Baden-Württemberg for academic assistants. The employment is temporary.

<ul> <li>📱 App-Programmierung mit Flutter </li> <li>✨ Mock-Up Erstellung für Apps </li> <li>🖊️ Dokumentation zur Verwendung und Weiterentwicklung von Apps </li> </ul>

Wir bieten:

<ul> <li> Spannende Tätigkeiten und Einblicke rund um das Thema autonomes Fahren </li> <li> Mitarbeit an aktuellen Forschungsthemen der Forschungsgruppe </li> <li> Bis zu 40 Stunden / Monat (Dauer: 6 Monate, Verlängerung möglich) </li> </ul>

Wir erwarten:

<ul> <li> Erste Erfahrungen im Programmieren </li> <li> Sehr gute Deutsch- oder Englischkenntnisse in Wort und Schrift </li> <li> Einsatzbereitschaft, Selbständigkeit, Kooperationsfähigkeit und Kreativität </li> </ul>

Erforderliche Unterlagen:

<ul> <li> Anschreiben (3-4 Sätze) </li> <li> Kurzer Lebenslauf (max. 2 Seiten) </li> <li> Auszug der aktuellen Studienleistungen </li> </ul>

<ul> <li>🐍 Python-Entwicklung zu Datensammlung und Auswertung </li> <li>🕸️ Unterstützung beim Entwurf der SoftwarearchitekturMock-Up Erstellung für Apps </li> <li>🖊️ Dokumentation zur Verwendung und Weiterentwicklung der Anwendungen </li> </ul>

Wir bieten:

<ul> <li> Spannende Tätigkeiten und Einblicke rund um das Thema autonomes Fahren </li> <li> Mitarbeit an aktuellen Forschungsthemen der Forschungsgruppe </li> <li> Bis zu 40 Stunden / Monat (Dauer: 6 Monate, Verlängerung möglich) </li> </ul>

Wir erwarten:

<ul> <li> Erste Erfahrungen im Programmieren </li> <li> Sehr gute Deutsch- oder Englischkenntnisse in Wort und Schrift </li> <li> Einsatzbereitschaft, Selbständigkeit, Kooperationsfähigkeit und Kreativität </li> </ul>

Erforderliche Unterlagen:

<ul> <li> Anschreiben (3-4 Sätze) </li> <li> Kurzer Lebenslauf (max. 2 Seiten) </li> <li> Auszug der aktuellen Studienleistungen </li> </ul>

You can support us as a student assistant and learn about the latest technologies and get an insight into research.

Your tasks are:

- Assembling hardware components (Arduino, R-Pi, e-Bike & driving simulator)

- Supporting the implementation of user studies (AR and VR)

- Extending software components (e.g. Python, C, C# and Unity)

- Testing of applications (Windows/Linux, AR/VR)

- Contributing your own ideas or assisting with brainstorming

You should be motivated and have an interest in new technologies as well as the automotive industry. Previous knowledge of any programming language as well as basic hardware knowledge is an advantage.

In return we offer you:

- Flexible working hours

- Industry and research related activities

- Contribution to publications

- Working with the latest technologies

- Coffee and great colleagues

1. You implement different approaches for fact-checking. 2. You conduct a literature review and develop proposals for further approaches.

You can support us as a student assistant and learn about the latest technologies and get an insight into research. Your tasks are:

- Assembling hardware components (Arduino, R-Pi, e-Bike & driving simulator)

- Supporting the implementation of user studies (AR and VR)

- Extending software components (e.g. Python, C, C# and Unity)

- Testing of applications (Windows/Linux, AR/VR)

- Contributing your own ideas or assisting with brainstorming.

You should be motivated and have an interest in new technologies as well as the automotive industry. Previous knowledge of any programming language as well as basic hardware knowledge is an advantage. In return we offer you:

- Flexible working hours

- Industry and research related activities

- Contribution to publications

- Working with the latest technologies

- Coffee and great colleagues

If you have successfully completed the course and have extensive knowledge and experience in this field, you have the opportunity to share your knowledge and help other students in understanding the concepts of artificial intelligence and developing practical applications. As a tutor for "Applications of Artificial Intelligence" in the winter semester 23/24, you will be able to offer individual support to the students, address their questions, and create a tailored learning environment. You can flexibly manage your schedule and deepen your expertise in this exciting field.

If you are interested in working as a tutor for the course "Applications of Artificial Intelligence" in the winter semester 23/24, please send an email with your CV and transcript of records to: shuzhou dot yuan at kit.edu

Since the network is also intended to be used in our autonomous vehicles in the long term, speed/efficiency plays a crucial role. Your tasks: <ul> <li>Research the current state of the art for traffic light detection and object detection in general.</li> <li>Conceptualize, create, and train the new network on the research group's hardware.</li> <li>If necessary, record new data with our autonomous vehicles. The test field Baden Württemberg in Karlsruhe allows direct communication with traffic lights, so time-consuming labeling tasks is likely not necessary.</li> <li>Integrate the network into the perception component of the FZI's driving stack for autonomous vehicles.</li> <li>Test and evaluate the network in the traffic of Karlsruhe and on campus east.</li> </ul>

You should have: <ul> <li>Experience in the field of machine learning, particularly deep learning for computer vision.</li> <li>Excellent knowledge of Python. Knowledge of C++ can be advantageous.</li> <li>Experience in PyTorch or good knowledge of other equivalent frameworks (Tensorflow, Jax).</li> <li>Knowledge of ROS is beneficial but not mandatory.</li> <li>Interest and commitment to the subject matter.</li> </ul>

These tasks can also be carried out as part of a master's thesis.

If you are interested, please email me with your current transcript and a brief description of why you would like to take on the position.

Nikolai.Polley@kit.edu

Tasks: Programming of the Cloud storage system in Java (basic Java knowledge is prefered).

Tasks

<ul> <li>Maintenance of student workstations (soft- and hardware)</li> <li>Automation of administrative tasks using scripts</li> <li>Support with design and implementation of migrating to a new authentication system</li> <li>Support with design and configuration of a multi-node container virtualization platform</li> </ul>

What we offer:

<ul> <li>Professional supervision</li> <li>Exciting tasks and insights on the topic of autonomous driving</li> <li>Up to 40 hours per month (up to 6 months, can be extended)</li> </ul>

What we expect:

<ul> <li>Proficiency with Linux / system administration</li> <li>Sound understanding of (TCP/IP-based) computer networks</li> <li>Experience in scripting with Python and Bash</li> <li>Experience with Docker or similar.</li> <li>Willingness and ability to acquire new technical knowledge</li> <li>Optional: experience with LDAP / ActiveDirectory and similar technologies.</li> </ul>

Required documents:

<ul> <li>Cover letter (3-4 sentences)</li> <li>Brief CV (max. 2 pages)</li> <li>Excerpt of latest academic achievements</li> </ul>

Tasks

<ul> <li>Implementation of object detection methods on different AD datasets</li> <li>Implementation of conversion between object lists and a graphical scenario representation</li> <li>Implementation of algorithms to extract relevant scenarios from heterogeneous sensor data</li> <li>Integration of simulation environments (esp. CARLA) with our own AD stack and / or open-source stack like Autoware or openPilot</li> <li>Support in design and implementation of Graph Neural Network (GNN)-based methods for representation, clustering and generation of scenarios</li> </ul>

What we expect:

<ul> <li>Very good knowledge and experience in Python</li> <li>Knowledge in the field of machine learning, especially Deep Learning</li> <li>Experience in the use of PyTorch and / or TensorFlow</li> <li>Basic Linux knowledge and experience</li> <li>Willingness and ability to acquire new technical knowledge and read scientific papers</li> </ul>

Required documents:

<ul> <li>Cover letter (3-4 sentences)</li> <li>Brief CV (max. 2 pages)</li> <li>Excerpt of latest academic achievements</li> </ul>

Tasks

<ul> <li>Research and compilation of current contents on the topics of Machine Learning and Artificial Intelligence</li> <li>Topics: Transformer, Large-Scale Training, Generative Models, Reinforcement Learning, Graph Neural Networks, Convolutional Neural Networks, Fundamentals, etc.</li> <li>Creation and design of lecture slides and supporting materials</li> <li>Development of exercise tasks and projects</li> <li>Collaboration with lecturers for the continuous improvement of lecture content</li> </ul>

We offer:

<ul> <li>Committed support by the chair team</li> <li>Interesting and varied topics and activities in the field of machine learning</li> <li>Up to 40 hours / month (Duration: 6 months, extension possible)</li> </ul>

We expect:

<ul> <li>Basic knowledge in machine learning</li> <li>Language skills in English and/or German</li> <li>Experience in the use of tools such as Python, Powerpoint</li> <li>Independent work style and creativity in the creation of materials</li> <li>Willingness and ability to engage in new topics and technologies</li> <li>Optional: Experience in teaching or tutoring</li> </ul>

Required documents:

<ul> <li>Cover letter (3-4 sentences)</li> <li>Short resume (max. 2 pages)</li> <li>Excerpt of current academic achievements</li> </ul>

CARLA, Microsoft AirSim, IPG CarMaker or NVIDIA DriveSim offer versatile possibilities for the simulation of sensor technology, vehicle dynamics, etc.

I am in search for a student (m/f/d) to support me with configuration, implementation and possibly extension of simulation environments (primarily CARLA).

Tasks

<ul> <li>Integration of our AV software stack with CARLA for testing and data collection</li> <li>Integration of available open-source AV software stacks (openPilot, Autoware, ...) with CARLA</li> <li>Implementation of scripts for dynamic creation and resimulation of traffic scenarios in CARLA</li> <li>Creation of new maps and assets for CARLA and Unreal 4</li> <li>General support in software development (primarily Python) in the context of simulations</li> </ul>

What we expect

<ul> <li>Good knowledge and experience with Python</li> <li>Basic Linux knowledge and experience</li> <li>Basic knowledge of 3D modeling (Blender) or willingness to acquire such</li> <li>Willingness and ability to acquire new technical knowledge and understand scientific papers</li> <li>Optional: Basic knowledge in the field of machine learning</li> </ul>

Required documents

<ul> <li>Cover letter (3-4 sentences)</li> <li>Brief CV (max. 2 pages)</li> <li>Excerpt of latest academic achievements</li> </ul>

Conditions

<ul>

 <li>Position: Student Research Assistant (Hiwi)</li>
 <li>Department: Institute of Applied Informatics and Formal Description Methods</li>
 <li>Supervisor: Marcus Fechner</li>
 <li>Location: Karlsruhe Institute of Technology (KIT)</li>

</ul>

About Us

At the research group “Applied Technical-Cognitive Systems”, we are at the forefront of deep learning in the context of applied machine intelligence. Our research is in the area of autonomous systems, from self-driving cars (CoCar NextGen, CoCar and the shuttles Anna and Ella) to autonomous service robots. We utilize deep learning and other machine learning based approaches to advance these fields.

Position Overview

<ul>

 <li>Position: Student Assistant</li>
 <li>Start Date: As soon as possible</li>
 <li>Working Hours: 20 - 80 hours per month</li>
 <li>Duration: 6 months with possibility of extension</li>

</ul>

Job Description

Developing generally capable reinforcement learning agents poses a significant challenge, especially in hard exploration tasks. Expert robotic data is scarce and expensive, but also reward functions are not easy to design for complex tasks. On the other hand, unlabeled expert video data is abundant, but not straightforward to learn behavioral priors from, as no labels exist (actions).

In this research, we want to investigate how we can train agents on unlabeled expert videos, such as YouTube videos, in a scalable way to master a wide variety of complex tasks, not solvable by conventional reinforcement learning. For support on this topic, we are searching for a motivated student.

Your primary responsibilities will include:

<ul>

 <li>Implementing and training deep learning models.</li>
 <li>Reading related research papers and participating in discussions on the topic.</li>
 <li>Collaborating with us on experimental design, execution, and evaluation.</li>
 <li>Other tasks as assigned related to our research.</li>

</ul>

Qualifications

<ul>

 <li>Current enrollment as a student at KIT.</li>
 <li>Strong interest in deep learning and machine learning.</li>
 <li>Knowledge of the programming language Python.</li>
 <li>Knowledge of PyTorch and/or Tensorflow.</li>
 <li>Experience in working with Linux and Git.</li>
 <li>Motivated to read research papers.</li>
 <li>Motivated, responsible, and a quick learner.</li>
 <li>Speak either German and/or English.</li>

</ul>

What we Offer

<ul>

 <li>Gain hands-on experience in the field of deep learning and conducting systematic research.</li>
 <li>Work closely with experienced researchers and PhD candidates.</li>
 <li>Weekly to bi-weekly meetings with supervisor.</li>
 <li>Coding support and helpful supervision.</li>
 <li>Flexible working hours to accommodate your class schedule and the option for working remotely.</li>
 <li>Contribute to research projects and papers.</li>
 <li>Access to top-of-the-line deep learning workstations with the latest GPUs.</li>

</ul>

How to Apply

If you are enthusiastic about deep learning and eager to contribute to our research, please send your application to marcus.fechner@kit.edu with the following documents:

<ol>

 <li>Cover letter (0.25-0.5 pages): Why do you want to work on this topic? Why are you suitable for the position? (mention your interests and relevant skills).</li>
 <li>CV/Resume (max. 2 pages).</li>
 <li>Recent transcript of records / grading table.</li>
 <li>Optional: Any relevant coding or project portfolio.</li>

</ol>

If you have any questions or need further information, please contact marcus.fechner@kit.edu. Natürlich auch gerne auf Deutsch :)

Conditions

<ul>

 <li>Position: Student Research Assistant (Hiwi)</li>
 <li>Department: Institute of Applied Informatics and Formal Description Methods</li>
 <li>Supervisor: Marcus Fechner</li>
 <li>Location: Karlsruhe Institute of Technology (KIT)</li>

</ul>

About Us

At the research group “Applied Technical-Cognitive Systems”, we are at the forefront of deep learning in the context of applied machine intelligence. Our research is in the area of autonomous systems, from self-driving cars (CoCar NextGen, CoCar, and the shuttles Anna and Ella) to autonomous service robots. We utilize deep learning and other machine learning-based approaches to advance these fields.

Position Overview

<ul>

 <li>Position: Student Assistant</li>
 <li>Start Date: As soon as possible</li>
 <li>Working Hours: 20 - 80 hours per month</li>
 <li>Duration: 6 months with the possibility of extension</li>

</ul>

Job Description

Dealing with the real world is challenging, as changing weather conditions, new objects, situations, etc. alter the data observed by the model. In practice, this data distribution shift or out-of-distribution data makes the model unreliable and hinders the safe deployment of deep neural networks in critical use cases, for example, autonomous driving. Models that fail to generalize in such scenarios may result in dangerous or catastrophic behavior.

In this research, we want to investigate how we can integrate different methods to estimate uncertainty in popular object detection models, to reliably detect when the model might fail. For support on this topic, we are searching for a motivated student.

Your primary responsibilities will include:

<ul>

 <li>Implementing and training deep learning models.</li>
 <li>Reading related research papers and participating in discussions on the topic.</li>
 <li>Collaborating with us on experimental design, execution, and evaluation.</li>
 <li>Other tasks as assigned related to our research.</li>

</ul>

Qualifications

<ul>

 <li>Current enrollment as a student at KIT.</li>
 <li>Strong interest in deep learning and machine learning.</li>
 <li>Knowledge of the programming language Python.</li>
 <li>Knowledge of PyTorch and/or Tensorflow.</li>
 <li>Experience in working with Linux and Git.</li>
 <li>Motivated to read research papers.</li>
 <li>Motivated, responsible, and a quick learner.</li>
 <li>Speak either German and/or English.</li>

</ul>

What we Offer

<ul>

 <li>Gain hands-on experience in the field of deep learning and conducting systematic research.</li>
 <li>Work closely with experienced researchers and PhD candidates.</li>
 <li>Weekly to bi-weekly meetings with supervisor.</li>
 <li>Coding support and helpful supervision.</li>
 <li>Flexible working hours to accommodate your class schedule and the option for working remotely.</li>
 <li>Contribute to research projects and papers.</li>
 <li>Access to top-of-the-line deep learning workstations with the latest GPUs.</li>

</ul>

How to Apply

If you are enthusiastic about deep learning and eager to contribute to our research, please send your application to marcus.fechner@kit.edu with the following documents:

<ol>

 <li>Cover letter (0.25-0.5 pages): Why do you want to work on this topic? Why are you suitable for the position? (mention your interests and relevant skills).</li>
 <li>CV/Resume (max. 2 pages).</li>
 <li>Recent transcript of records / grading table.</li>
 <li>Optional: Any relevant coding or project portfolio.</li>

</ol>

If you have any questions or need further information, please contact marcus.fechner@kit.edu. Natürlich auch gerne auf Deutsch :)

The tasks include:

• Research on quantitative methods for road traffic noise modeling

• Reviewing literature relevant to the project and the research that we do

• Implementing proofs of concept for the above tasks

• Communication with project partners and demonstrators

• Documenting all tasks and outcomes

Qualifications and Skills:

We seek highly motivated Bachelor or Master students in computer science, industrial engineering, statistics and econometrics, or related fields, preferably at KIT.

• Proficiency in Python and R, and readiness to learn other programming languages as needed

• Basic understanding of data science

• Bonus: familiarity with time series analysis, transportation science, simulation and modeling

• Strong communication skills for collaboration with diverse project partners

• Adequate English language proficiency for international teamwork

We offer:

• A part-time contract offering 30 to 60 hours per month, with salary tailored to the level of educational attainment. The initial contract duration is flexible, ranging from 3 to 6 months, with high likelihood for extension.

• Flexible working hours, also working at home possible.

• A variety of tasks so that you can learn a lot. Also, state-of-the-art research can be performed and will be published together with the student.

How to apply:

Please send your application to min-bin.lin@kit.edu with the following information:

• CV / Resume (max. 2 pages)

• List of courses or workshops taken

The tasks include:

• Research on quantitative methods for road traffic noise modeling

• Reviewing literature relevant to the project and the research that we do

• Implementing proofs of concept for the above tasks

• Communication with project partners and demonstrators

• Documenting all tasks and outcomes

Qualifications and Skills:

We seek highly motivated Bachelor or Master students in computer science, industrial engineering, statistics and econometrics, or related fields, preferably at KIT.

• Proficiency in Python and R

• Basic understanding of data science

• Bonus: familiarity with time series analysis, transportation science

• Strong communication skills for collaboration with diverse project partners

• Adequate English language proficiency for international teamwork

We offer:

• A part-time contract offering 30 to 60 hours per month, with salary tailored to the level of educational attainment. The initial contract duration is flexible, ranging from 3 to 6 months, with high likelihood for extension.

• Flexible working hours, also working at home possible.

• A variety of tasks so that you can learn a lot. Also, state-of-the-art research can be performed and will be published together with the student.

The tasks include:

• Research on quantitative methods for trajectory data

• Data storytelling and visualization

• Reviewing literature relevant to the project and the research that we do

• Implementing proofs of concept

• Communication with project partners and demonstrators

• Documenting all tasks and outcomes

Qualifications and skills:

We seek highly motivated Bachelor or Master students in computer science, industrial engineering, statistics and econometrics, geomatics, or related fields, preferably at KIT.

• Proficiency in Python and R, and readiness to learn other programming languages as needed

• Basic understanding of data science

• Bonus: familiarity with transportation science, autonomous driving

• Strong communication skills for collaboration with diverse project partners

• Adequate English language proficiency for international teamwork

We offer:

• A part-time contract offering 40 to 80 hours per month, with salary tailored to the level of educa-tional attainment. The initial contract duration is flexible, ranging from 3 months to beyond, with high likelihood for extension.

• Flexible working hours, also working at home possible.

• A variety of tasks so that you can learn a lot. Also, state-of-the-art research can be performed and will be published together with the student.

How to apply:

Please send your application to min-bin.lin@kit.edu with the following information:

• CV / Resume (max. 2 pages)

• List of courses or workshops taken

You can support us as a student assistant and learn about the latest technologies and get an insight into research. Your tasks are: - Assembling hardware components (Arduino, robot e.g. turtlebot3/ARI, R-Pi etc.) - Supporting the implementation of user studies (Human-Robot Interaction, Virtual/Augmented Reality) - Extending software components (e.g. Python, C, ROS(2) and Unity) - Testing of applications (e.g. deployment of multiple mobile robots ) - Contributing your own ideas or assisting with brainstorming.

You should be motivated and have an interest in new technologies as well as the automotive industry. Previous knowledge of any programming language as well as basic hardware knowledge is an advantage. In return we offer you: - Flexible working hours - Industry and research related activities - Contribution to publications - Working with the latest technologies - Coffee and great colleagues

Have we sparked your interest in Vision Zero?

Tasks

<ul>

 <li>Conception and implementation of web backend and frontend applications.</li>
 <li>Implementation of tooling and scripts for data processing in a scientific context</li>
 <li>Implementation of state-of-the-art (deep) machine learning algorithms.</li>

</ul>

What we offer

<ul>

 <li>Professional supervision</li>
 <li>Exciting tasks and insights on the topic of autonomous driving</li>
 <li>Up to 40 hours per month (up to 6 months, can be extended)</li>

</ul>

What we expect

<ul>

 <li>Practical experience with Python</li>
 <li>Practical experience with JavaScript / TypeScript (as well as HTML and CSS)</li>
 <li>Practical experience with a JavaScript-based frontend framework (preferably Vue.js)</li>
 <li>Basic understanding and best practices in software architecture / design</li>
 <li>Basic understanding of machine learning techniques, especially neural networks</li>
 <li>Optional: Experience with container technologies (Docker) and Linux</li>
 <li>Willingness and ability to acquire new technical knowledge</li>

</ul>

Required documents

<ul>

 <li>Cover letter (3-4 sentences)</li>
 <li>Brief CV (max. 2 pages)</li>
 <li>Excerpt of latest academic achievements</li>

Tasks

<ul>

 <li>Maintenance of student workstations (soft- and hardware)</li>
 <li>Automation of administrative tasks using scripts</li>
 <li>Support with design and implementation of migrating to a new authentication system</li>
 <li>Support with design and configuration of a multi-node container virtualization platform</li>

</ul>

What we offer

<ul>

 <li>Professional supervision</li>
 <li>Exciting tasks and insights on the topic of autonomous driving</li>
 <li>Up to 40 hours per month (up to 6 months, can be extended)</li>

</ul>

What we expect

<ul>

 <li>Proficiency with Linux / system administration</li>
 <li>Sound understanding of (TCP/IP-based) computer networks</li>
 <li>Experience in scripting with Python and Bash</li>
 <li>Experience with Docker or similar</li>
 <li>Willingness and ability to acquire new technical knowledge</li>
 <li>Optional: experience with Ansible</li>
 <li>Optional: experience with LDAP / AD and similar technologies</li>

</ul>

Required documents:

<ul>

 <li>Cover letter (3-4 sentences)</li>
 <li>Brief CV (max. 2 pages)</li>
 <li>Excerpt of latest academic achievements</li>