Job Offers

We are regularly looking for student assistants and offer HiWi activities at our Chair as well as in the Research Area Smart Data & Knowledge Services (SDS) at the DFKI. If you are interested, simply ask - even if no position is officially announced!

Context-Aware Recommender Systems for Personal Knowledge Assistants

Keywords:

  • context-aware recommender systems
  • graph embedding
  • information retrieval
  • knowledge worker actions and scenarios detection
  • personal information management
  • re-finding agents

Description:

One of the ways to assist knowledge workers in the daily tasks and improve their productivity is by providing them with relevant helpful information based on their current context. There are many challenges in developing such a recommender system which could be capable of recommending the right information at the right time to the right person. Understanding the contextual state of the users as precisely as possible, along with detecting their activities and information need play a significant role in enhancing the context-aware recommender systems for personal knowledge assistants. A mixture of knowledge and skills in various fields such as information retrieval, graph embedding, and machine learning is needed to tackle the existing challenges in this area.

Text Mining / Parallel Data Processing

Keywords:

  • Information Extraction
  • Knowledge Graphs
  • Large Scale Data Processing
  • Natural Language Processing
  • Web Crawling

Description:

Crawling large amounts of websites and web archives requires large scale data processing technologies, like parallel computing in the cloud (e.g. with PySpark). Various NLP tools then are used to automatically extract and classify important keywords from the crawled text, to build knowledge graphs or to classify the document as a whole. A major challenge here is to deal with inconsistencies and ambiguities in human language, written text and faulty HTML code.

Diffusion Models for Super-Resolution

Keywords:

  • Deep Learning
  • Denoising Diffusion Probabilistic Models
  • Single Image Super-Resolution

Description:

Denoising Diffusion Probabilistic Models (DDPMs) or Diffusion Models (https://arxiv.org/abs/2006.11239, https://arxiv.org/abs/2102.09672) became famous with tools like Dall-E and Stable Diffusion.
Unfortunately, many novel approaches require tremendous GPU power and memory. We want to change that by exploring new ways of optimization w.r.t. architecture, time step modifications and analysis of given methods. The application field of the DDPMs will be Single Image Super-Resolution.

Additional Sources: https://arxiv.org/abs/2104.07636 https://arxiv.org/abs/2104.14951 https://arxiv.org/abs/2112.02475 https://arxiv.org/abs/2111.05826 https://arxiv.org/abs/2108.02938

Graph Extraction/Generation from Diagrams

Keywords:

  • cnn
  • cv
  • dynamic system simulation

Description:

I am currently supervising students in multiple topics related to the extraction and simulation of Graph-Based Engineering Drawings from optical sources: Graph Extraction from Printed/Handdrawn Circuit Diagrams/Piping&Instumentation Diagrams, Generation of Hand-Drawn Diagrams, Interactive Circuit Detection and Simulation

Efficient & data type independent CUDA kernels

Keywords:

  • cuda
  • efficiency
  • gpu
  • ptx assembly

Description:

With typical GPU code all operands must have the same data type. However, the speed of most operations on GPUs is limited not by computation, but by memory bandwidth and latency. In these cases using the smallest possible data types can greatly enhance performance. The standard approach of compiling one version of a kernel for each combination inputs is infeasible though, since the number of combinations, especially for 3 or more inputs, leads to exorbitantly large binaries and very long compilation times. Much more desirable is a flexible kernel that can load inputs of arbitrary data type and converts them to an intermediate representation that can be used to perform the actual computation.

Note: This topic is highly specialized and requires deep knowledge in CUDA C++ programming and PTX assembly. Please only apply if you have previously worked with those languages.

Sherlock Holmes goes AI - Generative comics art of detective scenes and identikits

Keywords:

  • Bias in image generation models
  • Deep Learning Frameworks
  • Frontend visualization
  • Speech-To-Text, Text-to-Image Models
  • Transformers, Diffusion Models, Hugging Face

Description:

Sherlock Holmes is taking the statement of the witness. The witness is describing the appearance of the perpetrator and the forensic setting they still remember. Your task as the AI investigator will be to generate a comic sketch of the scene and phantom images of the accused person based on the spoken statement of the witness. For this you will use state-of-the-art transformers and visualize the output in an application. As AI investigator you will detect, qualify and quantify bias in the images which are produced by different generation models you have chosen.

Note:

This work is embedded in the DFKI KI4Pol lab together with the law enforcement agencies. The stories are fictional you will not work on true crime.

Requirements:

  • German level B1/2 or equivalent
  • Outstanding academic achievements
  • Motivational cover letter

Construction & Application of Enterprise Knowledge Graphs in the E-Invoicing Domain

Keywords:

  • knowledge graphs
  • knowledge services
  • linked data
  • semantic web

Description:

In recent years knowledge graphs received a lot of attention as well in industry as in science. Knowledge graphs consist of entities and relationships between them and allow integrating new knowledge arbitrarily. Famous instances in industry are knowledge graphs by Microsoft, Google, Facebook or IBM. But beyond these ones, knowledge graphs are also adopted in more domain specific scenarios such as in e-Procurement, e-Invoicing and purchase-to-pay processes. The objective in theses and projects is to explore particular aspects of constructing and/or applying knowledge graphs in the domain of purchase-to-pay processes and e-Invoicing.

Learning Analytics in Education

Keywords:

  • affective state
  • cognitive state
  • deep learning
  • machine learning

Description:

None

Next Thesis Topics