Practical trainings, student assistants and theses

Virtual Reality GUI for VR Microscopy Tool (Id 348)

Student Assistant

Foto: Microscenery VR volume rendering of life microscopy data ©Copyright: Jan TiemannThe Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. CASUS was founded in 2019 in Görlitz and conducts digital interdisciplinary systems research in various fields such as earth systems research, systems biology and materials science.

We are looking for a student assistant programmer (f/m/d) to implement a transfer function editor for virtual reality volume rendering application.

The project is based upon the kotlin framework scenery.

Further tasks could be building a two dimensional transfer function editor, other GUI features or networking components.

The student (f/m/d) should have sufficient English skills to be able to communicate with the team.

Institute: CASUS

Contact: Tiemann, Jan

Requirements

● Bachelor/Master candidate in computer science or a related field

Preferably the student (f/m/d) has prior knowledge in the following topics:

● Kotlin/Java
● (VR) UI design
● Volume Rendering
● working with scene graphs (like in any game engine)

Conditions

● A vibrant research community in an open, diverse, and international work environment.
● Scientific excellence and extensive professional networking opportunities.
● The place of work is Görlitz. (home office is possible)
● Compensation as student researcher (working hours to be determined).

Online application

Please apply online: english / german

Druckversion


Development of Quantum Generative Adversarial Learning Networks for Large-Scale Applications (Id 344)

Master theses / Diploma theses / Student Assistant

Foto: Quantum Circuit Diagram ©Copyright: Debanjan KonarThe Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. CASUS was founded in 2019 in Görlitz and conducts digital interdisciplinary systems research in various fields such as earth systems research, systems biology, and materials science.

We are looking for motivated, creative, and curious students (f/m/d) to help us in designing and simulating Quantum Generative Adversarial Learning Networks relying on hybrid classical-quantum algorithms for NISQ devices.

The scope of your job:
The Department "Matter under Extreme Conditions" at CASUS investigates how quantum machine learning algorithms can be applied in large-scale applications including material science and computer vision. We particularly work on hybrid-classical quantum algorithms and quantum optimization. In this project, you will investigate the feasibility of Quantum Generative Adversarial Learning Networks using Hybrid classical-quantum algorithms and Variational Quantum Circuits (VQCs). These algorithms rely on a hybrid classical-quantum circuit with gate parameters optimized during training. This involves improving the in-house software and combining it with larger software suites. Besides ease of use, another focus of these workflows should be reproducibility. Prior knowledge of quantum machine learning algorithm simulation is required!

Tasks for this project might involve

  • Literature research on existing solutions for the simulations of Quantum Generative Adversarial Learning Networks.
  • Development and improvement of the existing Quantum Generative Adversarial Learning (QuanGAN) Networks using the PennyLane Quantum Simulator.
  • Development of the learning procedure for QuanGAN for representing the probability distribution underlying large datasets and encoding them as a quantum state.
  • Integration of existing workflows in larger software suites in Python.

Institute: CASUS

Contact: Konar, Debanjan, Dr. Cangi, Attila

Requirements

● Bachelor/Master candidate in computer science or a related field.
● Experience with Machine learning, Deep learning, Python, IBM Q (Qiskit), PennyLane Quantum Simulator, PyTorch library.
● Ability and motivation to work in a team.
● Good language skills in English.
● Experience with scientific software development (optional).

Conditions

● A vibrant research community in an open, diverse, and international work environment.
● Scientific excellence and extensive professional networking opportunities.
● Compensation as student researcher (working hours to be determined).

Online application

Please apply online: english / german

Druckversion


Development of Quantum Generative Adversarial Learning Networks for Large-Scale Applications (Id 343)

Master theses / Diploma theses / Student Assistant

Foto: Quantum Circuit Diagram ©Copyright: Debanjan KonarThe Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. CASUS was founded in 2019 in Görlitz and conducts digital interdisciplinary systems research in various fields such as earth systems research, systems biology, and materials science.

We are looking for motivated, creative, and curious students (f/m/d) to help us in designing and simulating Quantum Generative Adversarial Learning Networks relying on hybrid classical-quantum algorithms for NISQ devices.

The scope of your job:
The Department "Matter under Extreme Conditions" at CASUS investigates how quantum machine learning algorithms can be applied in large-scale applications including material science and computer vision. We particularly work on hybrid-classical quantum algorithms and quantum optimization. In this project, you will investigate the feasibility of Quantum Generative Adversarial Learning Networks using Hybrid Classical-Quantum algorithms and Variational Quantum Circuits (VQCs). These algorithms rely on a hybrid classical-quantum circuit with gate parameters optimized during training. This involves improving the in-house software and combining it with larger software suites. Besides ease-of-use, another focus of these workflows should be reproducibility. Prior knowledge of quantum machine learning algorithm simulation is required!

The tasks for this project might involve:

  • Literature research on existing solutions for the simulations of Quantum Generative Adversarial Learning Networks.
  • Development and improvement of the existing Quantum Generative Adversarial Learning (QuanGAN) Networks using the PennyLane Quantum Simulator.
  • Development of the learning procedure for QuanGAN for representing the probability distribution underlying large datasets and encoding them as a quantum state.
  • Integration of existing workflows in larger software suites in Python.

Institute: CASUS

Contact: Konar, Debanjan, Dr. Cangi, Attila

Requirements

● Bachelor/Master candidate in computer science or a related field.
● Experience with Machine learning, Deep learning, Python, IBM Q (Qiskit), PennyLane Quantum Simulator, PyTorch library.
● Ability and motivation to work in a team.
● Good language skills in English.
● Experience with scientific software development (optional).

Conditions

● A vibrant research community in an open, diverse, and international work environment.
● Scientific excellence and extensive professional networking opportunities.
● Compensation as student researcher (working hours to be determined).

Online application

Please apply online: english / german

Druckversion


Development of an automation system for materials science simulations (Id 337)

Master theses / Diploma theses / Student Assistant

Foto: MALA ©Copyright: Dr. Attila CangiThe Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. CASUS was founded in 2019 in Görlitz and conducts digital interdisciplinary systems research in various fields such as earth systems research, systems biology, and materials science.

We are looking for motivated, creative, and curious students to help us automate generating simulation data for machine-learning projects in the field of matter under extreme conditions.

The scope of your job
The Department Matter under Extreme Conditions at CASUS investigates how materials properties can be predicted based on machine-learning algorithms. This requires large amounts of simulation data. Generating this data requires a large degree of user input. In this project, you will investigate if and how existing tools for automation in the field of materials science can be integrated into computational workflows to drastically speed up data acquisition. This involves improving the in-house software and combining it with larger software suites. Besides ease-of-use, another focus of these workflows should be reproducibility. No prior knowledge of materials science simulation is required!

Tasks for this thesis might involve:

  • Literature research on existing solutions for the automation of simulations
  • Development and improvement of the existing Python workflows
  • Integration of existing workflows in larger software suites
  • Development of a graphical user interface, potentially web based

Institute: CASUS

Contact: Fiedler, Lenz, Dr. Cangi, Attila

Requirements

  • Bachelor in computer science or related field
  • Experience with Python, JavaScript or Java
  • Ability to work in a team
  • Good language skills in English
  • Experience with software automation or database systems (optional)
  • Experience with Git or SVN (optional)
  • Experience with scientific software development (optional)

Conditions

  • A vibrant research community in an open, diverse, and international work environment
  • Scientific excellence and extensive professional networking opportunities
  • Compensation as student researcher (optional, working hours to be determined)

Online application

Please apply online: english / german

Druckversion