Semantische Suche

Wir bieten:

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

Wir erwarten:

• Erste Erfahrungen im Programmieren
• Sehr gute Deutsch- oder Englischkenntnisse in Wort und Schrift
• Einsatzbereitschaft, Selbständigkeit, Kooperationsfähigkeit und Kreativität

Erforderliche Unterlagen:

• Anschreiben (3-4 Sätze)
• Kurzer Lebenslauf (max. 2 Seiten)
• Auszug der aktuellen Studienleistungen

Wir bieten:

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

Wir erwarten:

• Erste Erfahrungen im Programmieren
• Sehr gute Deutsch- oder Englischkenntnisse in Wort und Schrift
• Einsatzbereitschaft, Selbständigkeit, Kooperationsfähigkeit und Kreativität

Erforderliche Unterlagen:

• Anschreiben (3-4 Sätze)
• Kurzer Lebenslauf (max. 2 Seiten)
• Auszug der aktuellen Studienleistungen

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

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

What we offer:

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

What we expect:

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

Required documents:

• Cover letter (3-4 sentences)
• Brief CV (max. 2 pages)
• Excerpt of latest academic achievements

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

What we expect:

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

Required documents:

• Cover letter (3-4 sentences)
• Brief CV (max. 2 pages)
• Excerpt of latest academic achievements

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

We offer:

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

We expect:

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

Required documents:

• Cover letter (3-4 sentences)
• Short resume (max. 2 pages)
• Excerpt of current academic achievements

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

What we expect

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

Required documents

• Cover letter (3-4 sentences)
• Brief CV (max. 2 pages)
• Excerpt of latest academic achievements

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

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

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

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:

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

Qualifications

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

What we Offer

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

How to Apply

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

1. 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).
2. CV/Resume (max. 2 pages).
3. Recent transcript of records / grading table.
4. Optional: Any relevant coding or project portfolio.

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

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

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

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

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:

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

Qualifications

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

What we Offer

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

How to Apply

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

1. 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).
2. CV/Resume (max. 2 pages).
3. Recent transcript of records / grading table.
4. Optional: Any relevant coding or project portfolio.

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