Practical trainings, student assistants and theses

Unterstützung im Rechnungswesen (Id 351)

Student Assistant

Die Abteilung Finanzen, Finanzcontrolling und Drittmittel ist für das Finanzmanagement des Helmholtz-Zentrum Dresden-Rossendorf verantwortlich. Im Bereich Rechnungswesen (Haupt-, Banken-, Debitoren-, Kreditoren- und Anlagenbuchhaltung) wird Ihre Hilfe benötigt.

Ihre Aufgaben:

  • Unterstützung (SAP) bei der Erfassung von Geschäftsvorfällen
  • Unterstützung (SAP) bei der Stammdatenpflege, insbesondere Kreditoren
  • Sonstige Unterstützungstätigkeiten

Department: Finance, Financial Controlling and Third-party Funds

Contact: Hartwig, Patrick

Requirements

  • Begonnenes Studium der Wirtschaftswissenschaften
  • Erste Kenntnisse in den Grundlagen des Rechnungswesens (Buchführung, Kosten- und Leistungsrechnung)
  • Selbstständige und verantwortungsvolle Arbeitsweise

Conditions

  • Arbeitsbeginn ab sofort
  • Mindestens 6 Monate

Wir bieten Ihnen die Möglichkeit, im Studium Erlerntes praxisnah umzusetzen! Es erwarten Sie ein
motiviertes und kollegiales Arbeitsumfeld, tatkräftige Unterstützung bei der Umsetzung Ihrer Aufgaben sowie spannende Einblicke in die finanztechnische Schaltzentrale unseres Forschungsstandortes.

Online application

Please apply online: english / german

Druckversion


Studentische Hilfskraft (w/m/d) Unterstützung bei der Digitalisierung von Bestandsakten (Id 350)

Student Assistant / Research Assistant

Foto: The Team Personal Service is here for you! ©Copyright: pixabay.comZur Unterstützung unserer Personalabteilung suchen wir ab sofort eine engagierte und verantwortungsbewusste studentische Hilfskraft (w/m/d). Das Team der Personalabteilung bietet eine ganzheitliche Betreuung aller Beschäftigten des Helmholtz-Zentrum Dresden-Rossendorf.

Deine Aufgaben:

  • Unterstützung beim Teilschritt Digitalisierung der Bestandsakten
  • Erstellung von Mitarbeiterlisten nach vorgegebenen Kriterien
  • Zusammenstellen der entsprechenden Akten
  • Verpacken in Kartons
  • Pünktliche Übergabe der Akten an einen externen Dienstleister
  • Kontrolle des vollständigen Aktenrücklaufs
  • Auspacken der Kartons
  • Einsortieren der Aktenrückläufe
  • Stichprobenhafte Kontrolle des digitalisierten Aktenbestands

Department: Personnel Affairs

Contact: Hübner, Franziska, Wobst, Daniela

Requirements

  • Du absolvierst dein Studium in den Studiengängen Wirtschaftswissenschaften, Geistes- und Sozialwissenschaften oder vergleichbar
  • Du hast die ersten Semester des Studiums erfolgreich absolviert
  • Du arbeitest gerne im Team und verfügst über eine hohe Einsatzbereitschaft
  • Du kannst dich sowohl in Deutsch als auch in Englisch sehr gut und sicher mündlich und schriftlich ausdrücken
  • Du verfügst über eine gute Auffassungsgabe, wodurch Du schnell in der Lage bist, selbständig zu arbeiten
  • Bisherige Erfahrungen durch Praktika in anderen Unternehmen und/oder HR sowie erste Kenntnisse im SAP HCM runden Dein Profil ab

Conditions

  • ein spannendes Arbeitsumfeld auf einem attraktiven Forschungscampus
  • faire Bezahlung, Flexibilität und Spaß bei deiner Arbeit in einem freundlichen Team
  • maximal 19 Arbeitsstunden pro Woche während der Vorlesungszeit, Aufstockung in der vorlesungsfreien Zeit möglich
  • eine gemeinsame Planung der Arbeitstage zur optimalen Vereinbarkeit von Studium und Praxis
  • einen sehr schönen Arbeitsplatz mitten in der Natur
  • zahlreiche Angebote des betrieblichen Gesundheitsmanagements
  • eine sehr gute Betriebskantine u.v.m.

Online application

Please apply online: english / german

Druckversion


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


Development of a user friendly graphic user interface for 3D automated mineralogy (Id 336)

Master theses / Student Assistant

Foto: 3D image of a crushed REE (red) bearing carbonate rock ©Copyright: Dr. Jose Ricardo da Assuncao GodinhoOur research team is developing a new method to analyse and measure the 3D properties of particles using X-ray computed tomography (CT). The method consists in preparing and scanning the sample (laboratory work), and processing the 3D image of the sample according to the sample’s characteristics. The image processing steps have been developed as a python code that now requires a user-friendly graphical interface (GUI) that users can use without programming knowledge. The resulting software will be the first to enable the automatic classification and quantification of the 3D properties of valuable minerals inside rocks and recyclable materials. This would be an impactful tool in the mining, minerals processing and recycling industries.

Tasks (6 months):

  • Develop a user-friendly GUI that can be used to input the necessary analysis parameters, to quantify the particle properties and to visualize the results using already developed image processing scripts.
  • Create statistical and machine learning analysis visualization tools to link the different particle properties, and incorporate them into the GUI.
  • Possibility to obtain experience in sample preparation and analysis using CT.

Department: X-ray and bulk analytics

Contact: Dr. da Assuncao Godinho, Jose Ricardo, Gupta, Shuvam

Requirements

  • Ongoing degree in computer sciences (or similar) with an interest in raw materials and 3D imaging; or degree in Earth sciences (or similar) with demonstrable computer skills
  • Work knowledge with Javascript or similar language
  • Experience or desire to learn about developing graphical user interfaces
  • Experience using large data analysis and visualization software is beneficial (e.g. using Orange3, spark, etc)

Conditions

  • Place of work is the Freiberg campus

Online application

Please apply online: english / german

Druckversion


Student assistant at the DeltaX School Lab (Id 308)

Student Assistant / Research Assistant

Foto: Schülerlabor DeltaX - Experimentiertage Magnetismus ©Copyright: André WirsigThe DeltaX student laboratory makes research at the Helmholtz-Zentrum Dresden-Rossendorf an experience for students. We are looking for tutors who enjoy teaching science, research and technology and who would like to support students conducting their experiments. Apply as a student assistant in the DeltaX school laboratory and become part of a young and open-minded team.

Department: School Lab DeltaX

Contact: Dr. Streller, Matthias, Gneist, Nadja

Requirements

  • Study of a scientific subject
  • Remaining study duration of at least 2 semesters
  • Pleasure in teaching science and research- Good to very good grades
  • Very good knowledge of German (B2 / C1 level)

Conditions

  • 5 - 10 h / week on whole weekdays
  • Start of hiring according to agreement

Links:

Online application

Please apply online: english / german

Druckversion