Ihre Aufgaben

Grundlagen- und anwendungsorientiere Forschung im Bereich Neuromorphic Computing und Sensing

Sensor-Simulation in vorhanden Frameworks; Implementierung von adaptiven Sensoren

Arbeit in einem dynamischen, internationalen, und innovativen Arbeitsumfeld mit exzellenter Ausstattung

Simulation von Event-Daten und Training von Neuronalen Netzen mit diesen Daten

Präsentation von Ergebnissen auf Projektebene und auf internationalen Konferenzen und in Journalen

Die Möglichkeit zur Promotion ist gegeben

Unsere Anforderungen

Abgeschlossenes Masterstudium der Informatik, oder einem verwandten Fachgebiet

Fundierte Kenntnisse im Bereich Maschinelles Lernen, insbesondere Deep Learning

Idealerweise Erfahrung mit Neuromorphic Computing und/oder Event-Kameras

Gute Programmierkenntnisse in mindestens einer etablierten Programmiersprache

Kreativität, Selbstständigkeit, Teamgeist, und eine proaktive Einstellung

Sehr gute deutsche und englische Sprachkenntnisse

Das Deutsche Forschungszentrum für Künstliche Intelligenz GmbH (DFKI) wurde 1988 als gemeinnützige Public-Private-Partnership (PPP) gegründet. Das DFKI verbindet wissenschaftliche Spitzenleistung und wirtschaftsnahe Wertschöpfung mit gesellschaftlicher Wertschätzung. Das DFKI forscht seit über 35 Jahren an KI für den Menschen und orientiert sich an gesellschaftlicher Relevanz und wissenschaftlicher Exzellenz in den entscheidenden zukunftsorientierten Forschungs- und Anwendungsgebieten der Künstlichen Intelligenz. In der internationalen Wissenschaftswelt zählt das DFKI zu den wichtigsten „Centers of Excellence“.

Schwerbehinderte Bewerberinnen und Bewerber und Gleichgestellte werden bei gleicher Eignung besonders berücksichtigt. Das DFKI beabsichtigt, den Anteil von Frauen im Wissenschaftsbereich zu erhöhen und fordert deshalb Frauen ausdrücklich auf, sich zu bewerben.

Your tasks

Basic and application-oriented research in the field of neuromorphic computing and sensing

Simulation of event sensors in existing frameworks; implementation of adaptive sensor behavior

Work in a dynamic, international, and innovative working environment with excellent equipment

Training of neural networks with simulated event data

Presentation of results on project level and at international conferences and in journals

The possibility to do a PhD is given

Your qualifications

Master’s degree in Computer Science, or a related field.

Sound knowledge in the field of machine learning, especially deep learning

Ideally experience with neuromorphic computing and/or event cameras

Good programming skills in at least one established programming language

Creativity, independence, team spirit, and a proactive attitude

Good German and English language skills

The German Research Center for Artificial Intelligence (DFKI) has operated as a non-profit, Public-Private-Partnership (PPP) since 1988. DFKI combines scientific excellence and commercially-oriented value creation with social awareness and is recognized as a major “Center of Excellence” by the international scientific community. In the field of artificial intelligence, DFKI has focused on the goal of human-centric AI for more than 35 years. Research is committed to essential, future-oriented areas of application and socially relevant topics.

DFKI encourages applications from people with disability; DFKI intends to increase the proportion of female employees in the field of science and encourages women to apply for this position.

GANs have made significant strides since their inception in 2014, demonstrating remarkable capabilities in generating realistic audio and video mixes, as well as complex geometries. However, a persistent challenge lies in ensuring the accuracy of the generated results. While the visual appeal of these results may be convincing, they often fail to faithfully represent the genuine geometric properties they aim to emulate. A pertinent example is the simulation of paper folding and crumpling, where maintaining the inherent geometric characteristics of the sheet (e.g., preventing stretching) is crucial. Although deterministic simulations of paper deformation have been developed, comprehending and replicating them often necessitate an extensive understanding of material physics, mathematics, and computer graphics. One potential approach to address this challenge involves harnessing the power of GANs or other network architectures, such as variational autoencoders, to analyze 3D geometries and generate highly accurate representations. However, several hurdles must be overcome, including operating effectively within the 3D space and establishing a robust methodology to evaluate the fidelity of the output geometries. An intriguing application of such methods for generating 3D geometries lies in their ability to rapidly generate synthetic data with exceptional accuracy. This synthesized data can then be employed for training purposes in various domains, offering a valuable resource for enhancing learning algorithms and expanding their applicability.

Tasks

• State-of-the-art review on GANs and paper simulation
• Generating paper geometries using simulation
• Use GANs to generate geometries and designing accuracy and comparison methodology
• Evaluating generated results from GANs and possible refinement strategies

Requirements

• 3D computer vision, Computer graphics, Geometric modelling
• Deep Learning (TensorFlow, PyTorch, Keras)
• C++ (OpenGL, OpenCV), Python, C#

References

• Goodfellow, I., Pouget-Abadie, 2020. Generative adversarial networks. Communications of the ACM, 63(11), pp.139-144.
• Narain, R., 2013. Folding and crumpling adaptive sheets. ACM Transactions on Graphics (TOG), 32(4), pp.1-8.
• Smith, E.J. ,2017, October. Improved adversarial systems for 3d object generation and reconstruction. In Conference on Robot Learning (pp. 87-96). PMLR.
• Xiao, H., 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms.
• Das, S., 2019. Dewarpnet: Single-image document unwarping with stacked 3d and 2d regression networks.

Apply here.