Practical trainings, student assistants and theses

Froth flotation is a widely applied process in the separation of materials. There, the froth phase which consists of foam with particles has a
tremendous impact on the overall process performance. An efficient control in such processes requires suitable measurement systems. However, resulting from the opaque nature of such multiphase systems, on-line monitoring of the froth in industrial settings displays a major challenge and is mostly done by means of optical systems.
As an alternative for froth characterization, the use of acoustic measurements could provide a simple solution. It was observed, that a sound wave which is sent towards the froth/air interface will be reflected and the strength of the reflected signal contains information on the froth composition. This has the potential for advanced measurement systems.
In the next step, a deeper understanding of the relationship between reflected signal strength and the froth composition is required. Additionally, the influence of the froth surface has to be studied in more detail. The work aims at investigating this relationship and the influencing parameters in a laboratory flotation cell. This includes acquisition and processing of ultrasonic signals and parallel optical measurement of the froth's surface.

• Field of study: process engineering, mechanical engineering, or similar focus in chemistry or physics
• Interest in experimental work
• Experience with data processing using python is beneficial

• Work in multidisciplinary and international environment
• Compensation as for HZDR conditions
• Duration: at least 3 months

As a member of the Helmholtz Association of German Research Centers, the HZDR employs about 1,400 people. The Center's focus is on interdisciplinary research in the areas energy, health and matter. The Institute of Fluid Dynamics is conducting basic and applied research in the fields of thermo-fluid dynamics and magnetohydrodynamics in order to improve the sustainability, the energy efficiency and the safety of industrial processes.

Multiphase flows are important part of many industrial applications, whereas modelling of them is a challenging and complex task. For flow situations with higher void fractions, HZDR developed a new generalized concept for the CFD-simulations including flow regime transitions. The GENTOP (Generalized Two-Phase Flow) approach is able to simulate co-existing large-scaled (continuous) and small-scaled (polydispersed) structures (Fig. 1). Previous results were performed with the CFD code CFX and compared against DEBORA validation data.

The goal of the thesis would be to apply and improving the existing state of the simulations in the Fluent GENTOP framework.

We offer an interesting task dealing with complex physical phenomena, work in an international team using state-of-the-art calculation and programming methods.

We are looking for a motivated student (f/m/d) (master thesis) able to perform CFD simulations, understand and program code to generalize/parametrize CFD simulations, work with experimental data sets, document and present the work in an appropriate manner. Useful but not required is a knowledge of the following software tools: CFD codes CFX and Fluent, Python, GIT.

The task is supervised by Framatome and HZDR.

FRAMATOME is a designer and supplier of nuclear steam supply system and nuclear equipment, services and fuel for high levels of safety and performance. Framatome is a major international player in the nuclear energy market recognized for its innovative solutions and value-added technologies for designing, building, maintaining, and advancing the global nuclear fleet. The company designs, manufactures, and installs components, fuel and instrumentation and control systems for nuclear power plants and offers a full range of reactor services. With 14,000 employees worldwide, every day Framatome's expertise helps its customers improve the safety and performance of their nuclear plants and achieve their economic and societal goals.

• Studies in Engineering, Computer Science or comparable
• Interest in numerical work
• Good communication skills in both written and spoken English
• Useful but not required is a knowledge of the following software tools: CFD codes CFX and Fluent, Python, GIT.

• 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).
• Working place will be Dresden and/or Erlangen Germany.

Trickle bed reactors are subjected to dispersion phenomena on both the gas and liquid phase. In the last decades, several models have been proposed to describe the hydrodynamics of trickle bed reactors, involving up to six parameters, as reviewed by Gianetto et al. (1978). However, the axial dispersion model remains the most practical, accepted and used one. This model assumes dispersion as a stochastic phenomenon and uses the axial liquid dispersion coefficient as the sole parameter.
Traditionally, axial dispersion coefficients are measured using the residence time behaviour of inserted tracer substances. However, such methods are hardly universally applicable considering also production units. Tracer substances can alter the physical properties of the system and may cause detrimental impurities or process downtimes.
The flow modulation technique (FMT) has been proposed by Hampel (2015) and was recently applied by Do ß et al. (2017) for measuring the axial gas dispersion coefficient in bubble columns. However, it has never been applied for measuring the axial liquid dispersion coefficient in trickle bed reactors. Using the FMT, no tracer substances are injected; instead, a marginal sinusoidal modulation is superimposed to the inlet flow rate and used as a virtual tracer. This way, the modulation introduces a sinusoidal variation of the liquid holdup in time, called holdup wave. Along the reactor, the holdup wave gets damped in amplitude and shifted in phase due to liquid dispersion. Amplitude damping and phase shift can be measured and related to the value of the axial liquid dispersion coefficient via the above mentioned dispersion model. A schematic sketch of the working principle of liquid flow modulation is shown in the figure below. The approach is a new, non invasive and easy to apply experimental approach.
The candidate (f/m/d) will experimentally test the applicability of the (liquid) flow modulation approach in trickle bed reactors of different sizes, using gamma ray densitometry as a measurement technique. For comparison purposes, conductivity based tracer measurements will be performed as well. Liquids of different viscosities will be tested and the impact of viscosity on the value of the liquid axial dispersion coefficient will be evaluated.

• Studies in chemical engineering or comparable
• Interest in experimental work
• Good communication skills in both written and spoken English

• Start in September/October 2022
• Work in multidisciplinary and international environment
• Compensation as for HZDR conditions

The 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.

● 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).

● 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).

The 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.

● 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).

● 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).

Various metals, semiconductors, and oxides form regular nanoscale surface patterns in a complex process of self-assembly under low energy ion irradiation. While the elemental semiconductors Si and Ge have been extensively studied in this respect, there is no such investigation for alloys of Si and Ge. We want to explore which nanoscale pattern morphologies can emerge on SiGe surfaces and how they can be modified via the conditions of ion irradiation. We expect to obtain new insights into the complex process of ion-induced nanopattern formation in technologically relevant materials.
This work comprises the preparation of nanopatterned surfaces by low energy ion irradiation, imaging these surfaces surfaces by atomic force microscopy and electron microscopy, the quantitative analysis of these data, as well as simulating the patterning process based on continuum equations or kinetic MonteCarlo models.
The project provides an introduction to research at a large scale facility (Ion Beam Center IBC) and opportunities for networking with HZDR specialists (f/m/d) on nanoscale surface modification and characterization.

-- completed B.Sc. studies or Vordiplom in experimental physics, materials science, or related subject
-- good command of German and/or English
-- ability to work independently and systematically

-- place of work: HZDR, location Rossendorf
-- project duration: 12 months, flexible starting time

Various metals, semiconductors, and oxides form regular nanoscale surface patterns in a complex process of self-assembly under low energy ion irradiation. Depending on the experimental conditions nanopatterns of very different morphologies will form. They can be categorized into either the erosive or diffusive regime – depending on the dominant mass transport processes on the surface. For compound semiconductors the erosive regime has rarely been investigated so far. We want to find out under which conditions the expected nanopattern formation in the diffusive regime takes place. We expect to obtain new insights into the complex process of ion-induced nanopattern formation in technologically relevant materials.
This work comprises the preparation of nanopatterned surfaces by low energy ion irradiation, imaging these surfaces surfaces by atomic force microscopy and electron microscopy, the quantitative analysis of these data, as well as simulating the patterning process based on continuum equations or kinetic MonteCarlo models.
The project provides an introduction to research at a large scale facility (Ion Beam Center IBC) and opportunities for networking with HZDR specialists (f/m/d) on nanoscale surface modification and characterization.

-- completed B.Sc. studies or Vordiplom in experimental physics, materials science, or related subject
-- good command of German and/or English
-- ability to work independently and systematically

-- place of work: HZDR, location Rossendorf
-- project duration: 12 months, flexible starting time

Various metals, semiconductors, and oxides form regular nanoscale surface patterns in a complex process of self-assembly under low energy ion irradiation. Studies of the elemental semiconductors Si and Ge have shown that the symmetry of their crystalline surface strongly influences the morphology of those nanopatterns. However, only one particular surface orientation has been studied analogously for the compound semiconductors GaAs and InAs. While for these materials, the nanopattern morphology is mainly attributed to their compound character, a significant additional influence of the surface crystal structure is expected. We want to demonstrate this by investigation the ion-induced pattern formation on crystalline GaAs and InAs with various surface orientations. The resulting surface patterns may find application in the bottom-up fabrication of complex nanostructured systems.
This work comprises the preparation of nanopatterned surfaces by low energy ion irradiation, imaging these surfaces by atomic force microscopy and scanning tunnelling microscopy, the quantitative analysis of these data, as well as simulations of the patterning process based on continuum equations or kinetic MonteCarlo models.
The project provides an introduction to research at a large scale facility (Ion Beam Center IBC) and opportunities for networking with HZDR specialists (f/m/d) on nanoscale surface modification and characterization.

-- completed B.Sc. studies or Vordiplom in experimental physics, materials science, or related subject
-- good command of German and/or English
-- ability to work independently and systematically

-- place of work: HZDR, location Rossendorf
-- project duration: 12 months, flexible starting time

Ensembles of nanoscale metallic objects such as Ag nanocubes exhibit particular optical properties, which can be influenced by size, shape and spatial arrangement of these objects. Ion beam based techniques enable the preparation of nanopatterned surfaces, on which Ag nanocubes can be arranged in a regular fashion, as well as the modification of the nanocube shape by ion erosion. Thus the effects of changes in arrangement and shape on the optical properties of the ensemble can be studied.
This work comprises the preparation of nanopatterned surfaces by low energy ion irradiation, the arrangement of Ag nanocubes on such surfaces and their deformation by ion beam erosion, the imaging of theses sample systems by atomic force microscopy and scanning electron microscopy, the measurement of optical properties by cathodoluminescence and ellipsometry, and the quantitative analysis of the obtained data.
The project provides an introduction to research at a large scale facility (IBC) and opportunities for networking with HZDR specialists (f/m/d) on nanoscale surface modification and characterization.

-- completed B.Sc. studies or Vordiplom in experimental physics, materials science, or related subject
-- good command of German and/or English
-- ability to work independently and systematically

-- place of work: HZDR, location Rossendorf
-- project duration: 12 months, flexible starting time

Die gezielte Behandlung von Tumorerkrankungen erlangt zunehmend an Bedeutung. Die eng mit der radiopharmazeutischen Forschung verknüpfte Nuklearmedizin ist auf die Anwendung radiomarkierter Verbindungen (Radiopharmaka) für die Tumordiagnostik und -therapie spezialisiert. Dabei wird ein bestimmtes Radionuklid entweder direkt am Molekül oder stabil in einem Komplexbildner gebunden und an ein biologisch aktives Molekül geknüpft (Peptid, Antikörper...). Das Radiopharmakon bindet dann spezifisch an bestimmten Zellen (z.B. Knochenzellen, Tumorzellen...). Während zur diagnostischen Bildgebung Gamma- und Positronenemitter eingesetzt werden, kommen für therapeutische Anwendungen ausschließlich Betaemitter und Alphaemitter zum Einsatz. Für den Alphaemitter Actinium-225 steht, sofern der Chelator macropa verwendet wird, gegenwärtig kein geeignetes diagnostisches Radionuklid zur Verfügung.

In diesem Forschungsprojekt sollen Konjugate hergestellt werden, welche mit einem bildgebenden Radionuklid (Fluor-18, Iod-123, Lanthan-133) radiomarkiert werden können. Die Konjugate sollen sich gleichermaßen für die stabile Bindung von Actinium-225, welches therapeutisch angewendet wird, eignen. Nach erfolgreicher Synthese und Charakterisierung von definierten Zielverbindungen sollen diese radiomarkiert, und die Stabilität der radiomarkierten Substanzen im biologischen System beurteilt werden. Die vielversprechendsten Konjugate sollen anschließend auf zellulärer Ebene und schlussendlich präklinisch in vivo evaluiert werden.

• Studium der Chemie oder eines artverwandten Studiengangs
• Erfahrungen im Bereich der Synthesechemie und Analytik
• Interesse an der wissenschaftlichen Arbeit in einem interdisziplinären Team
• Bereitschaft zum Umgang mit Radioaktivität

• Beginn ist nach Absprache ab sofort möglich
• Praktikumsdauer mindestens 8 Wochen
• Vergütung nach internen HZDR-Regelungen

The 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

• 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)

• 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)

Our 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.

• 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)

• Place of work is the Freiberg campus

Data acquisition in large industrial vessels such as biogas fermenters or wastewater treatment plants is limited to local measurement points due to limited access to the vessel and the non-transparency of the fluid. To optimize these kinds of plants, the three-dimensional flow field and the spatial distribution of fluid properties such as temperature and electrical conductivity inside the vessel must be known. This can be achieved by the autonomous flow-following sensor particles developed by the HZDR. Equipped with a pressure sensor, an accelerometer, two gyroscopes and a magnetometer, the sensor particle can track the movement inside the vessels and derive the flow field from that. Additionally, the sensor particle gets position information by an ultra-wide-band based localization module (like GPS) as soon as it is on the fluid surface. The motion of the sensor particle is currently tracked with an error-state Kalman filter and yields a reliable tracking of the velocity and position, respectively. However, the tracking time is limited by the propagation of uncertainties of the inertial sensors through the filter. The objective of this master thesis is to extend this tracking time by the use of more advanced tracking algorithms like particle filter or other types of Kalman filters. This includes the following tasks:

• Literature review of advanced filters for motion tracking
• Theoretical comparison and implementing the most promising algorithm in Python
• Verification and performance analysis based on experimental data

Studies in the area of electrical, mechatronic, mechanical engineering or similar

• Basics of measurement uncertainty, digital signal processing
• Data analysis in Python
• Independent and structured way of working

• Start possible at any time
• Duration according to the respective study regulations

Liquid jets are unstable and eventually form droplets to minimize the surface energy with the surrounding fluid. The transition from dripping to jetting and dynamics of the droplet pinch-off have been studied extensively for various systems, from pure Newtonian fluids to complex non-Newtonian liquids. The jetting process has received significant attention as it is a critical step in various three-dimensional (3D) printing techniques such as dropwise additive manufacturing and the direct ink writing method. In most of the applications surface active materials such as surfactants, nanoparticles, and polymers exist in the systems. The presence of surface-active materials reduces the liquid-fluid surface energies and in some cases generates a viscoelastic layer at the interface.
In this research, we aim to study the influence of interfacial viscoelasticity on the dripping to jetting transition. The study is conducted by the injection of an aqueous phase (nanoparticle dispersions) into an oil phase that contains surfactants over a wide range of flow rates. We tune the magnitude of interfacial viscoelasticity by changing the concentration of surfactants and nanoparticles.
Research question:
Does the dripping to jetting transition (critical flow rate) linearly increase by increasing the interfacial viscoelasticity?

Experiments:
1. Measurements of interfacial tension and surface elasticity for a range of particle and surfactant concentration using Profile analysis tensiometry, and Langmuir trough.
2. Dripping to jetting experiments for the selected systems using high-speed cameras and in-house setups.

• Field of study: chemical engineering, process engineering, fluid mechanics, or similar focus in chemistry or physics
• Experience with laboratory work and imaging measurement techniques is beneficial

• Working in an international team
• Duration: at least 6 months
• Location: Dresden-Rossendorf

Das Institut für Fluiddynamik des Helmholtz-Zentrums Dresden-Rossendorf (HZDR) beschäftigt sich unter anderem mit Fragen der Modellbildung und Simulation von verfahrenstechnisch eng gekoppelten Power-to-X-Systemen bestehend aus den Teilprozessen Hochtemperaturelektrolyse (Solid Oxide Electrolyzer Cells) und heterogen katalysierten Syntheseprozessen von synthetischen Energieträgern der Zukunft (Methanol, Methan, usw.) unter stofflicher Nutzung anthropogener Kohlenstoffdioxidemissionen und regenerativ produziertem Strom. Auf Basis eines bereits existierenden Modells eines Power-to-Methanol Prozesses und eines ebenfalls vorliegenden techno-ökonomischen Teilmodells (TEA) soll die Wirtschaftlichkeit der dezentralen Produktion von Methanol mit Hilfe von erneuerbarem Strom in Kopplung mit großen Batteriespeichern untersucht werden.
Zur Realisierung dieser Aufgabe bietet die Abteilung Experimentelle Thermofluiddynamik für Studenten der unten genannten Studiengänge studienbegleitende Tätigkeiten zur beschriebenen Thematik an. Die Voraussetzung ist die Anfertigung einer Diplom- oder Masterarbeit.

Folgende Teilarbeiten sind durchzuführen:

• Literaturrecherche zu hybriden Energiespeichersystemen basierend auf Power-to-Methanol Prozessen und Batteriespeichern hinsichtlich Prozessdesign und Wirtschaftlichkeit,
• Literaturrecherche zur mathematisch-physikalischen Modellierung von Batteriespeichern und Erstellung eines einfachen Batteriespeichermodells mittels Matlab,
• Ermittlung der ökonomischen Randbedingungen für großskalige Batteriespeicher auf Basis von Literaturdaten,
• Untersuchung der Wirtschaftlichkeit des hybriden Energiespeichersystems für ein vorgegebenes Anschlussszenario für den Betrieb mit erneuerbaren Energiequellen.

• Student (w/m/d) der Studiengänge Wirtschaftsingenieurwesen, Chemieingenieurwesen, Verfahrenstechnik, Energietechnik, Maschinenbau oder ähnlicher fachlicher Ausrichtung,
• Grundkenntnisse in Matlab wünschenswert,
• Sorgfältige, kreative und selbstständige Arbeitsweise,
• Gute Sprachfertigkeiten (oral/schriftlich) in englischer und deutscher Sprache,
• Freude an der wissenschaftlichen und eigenständigen Arbeit.

Bearbeitungszeit: 6 Monate (Beginn ab sofort)

Distillation columns are used for the thermal separation of multicomponent mixtures in the chemical industry. Owing to increased energy supply from renewable sources a more flexible operation of such apparatuses is already demanded. However, enlarged over- and under load modes are challenging with respect to design, since a high separation efficiency needs to be attained anyway. Especially in case of fixed valve trays there are presently no reliable methods for estimating the influence of the tray design on the complex two-phase flow of liquid and vapor. Therefore, a current research project aims at detailed investigations of the two-phase flow at single valves in order to evaluate their performance.
Within the frame of a student internship experimental investigations will be carried out using an existing lab-scale test rig. The phase distribution of gas and liquid will be measured around different valves with high temporal and spatial resolution by specifically developed sensors. Subsequently, measured data will be used to quantify and model the momentum transfer from the gas inlets to the liquid phase. The results will be used in future developments of numerical models to predict the two-phase flow on such trays.

• studies in chemical/process/energy/mechanical engineering
• interest in experimental work
• creativity
• good written and oral communication skills in English and German

• start: from Sept. 2022
• working in a multi-disciplinary team
• remuneration according to HZDR internal regulations

Wire-mesh sensors allow for measuring the phase distribution of gas-liquid flows with high spatial and temporal resolution. For industrial applications (e.g. in power plants or chemical plants) previous projects already focussed on the development of data processing algorithms that convert the huge data sets into user friendly information, i.e. average void fraction and flow pattern. Hence, plant operators can now benefit from advanced process monitoring and operate their processes more efficiently.
The current developments focus on an enhanced system that includes online reference measurements in order to compensate drifts, which may result from changes of fluid properties when dealing with dynamic operation modes of the process. For this purpose, an existing concept should be converted into a final design, constructed and finally tested within the frame of an internship.

• interest in practical work
• manual skills
• creativity
• studies in process/energy/mechanical/electrical engineering
• good written and oral communication skills in English AND German

• start: immediately
• working in a multi-disciplinary team
• remuneration according to HZDR internal regulations

The 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.

• 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)

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

Wir beschäftigen uns mit der Entwicklung von PET-Radiotracern, die Rezeptoren im Tumormikromilieu (TME = tumor microenvironment) für die Diagnostik und Therapie von Krebs sichtbar machen. Dazu werden geeignete tumoraffine Leitstrukturen identifiziert (niedermolekulare organische Moleküle, Peptide und Peptidomimetika), synthetisiert und mit einem geeigneten Radionuklid kovalent (z.B. Fluor-18, Iod-123) oder über einen Chelator (z.B. Gallium-68, Lutetium-177) markiert. Diese Radioliganden werden in vitro an Tumorzelllinien und in vivo im Tiermodell hinsichtlich einer Anwendung in der Nuklearmedizin getestet. Langfristiges Ziel ist die Translation der entwickelten Radiotracer in die Klinik als Diagnosewerkzeug (PET/CT) oder nach Markierung mit einem Beta- oder Alphastrahler für die Endoradiotherapie von Tumorerkrankungen.
Im Rahmen eines Studentenpraktikums oder einer Abschlussarbeit (Bachelor/Master/Diplom) sollen organische Wirkstoffmoleküle synthetisiert und für eine anschließende radiochemische Markierung modifiziert werden. Die neuen Radioliganden werden dann biologisch in vitro und in vivo untersucht.

• Studium der Chemie mit abgeschlossenem Bachelor
• Gute Noten in organischer Synthesechemie
• Fähigkeit sich in ein interdisziplinäres Wissenschaftler-Team einzugliedern
• Bereitschaft zum Umgang mit Radioaktivität
• Gute Kenntnisse der deutschen und englischen Sprache

• Beginn nach Absprache jederzeit möglich
• Praktikumsdauer mindestens 8 Wochen, mit möglichst täglicher Anwesenheit (keine wiss. Hilfskräfte)
• Vergütung erfolgt nach HDZR-Richtlinien

Turmkraftwerke stellen die neueste Generation von Anlagen zur solarthermischen Elektroenergieerzeugung dar. Extrem konzentriertes Sonnenlicht wird dabei auf einen zentralen Absorber gerichtet, der die Wärme auf eine Wärmeträgerflüssigkeit überträgt (s. Foto). Zur Erhöhung des Wirkungsgrades von Turmkraftwerken soll die Arbeitstemperatur von derzeit maximal 550°C deutlich erhöht werden. Dafür sollen werkstoffwissenschaftliche Lösungen weiter verfolgt werden, die im Rahmen eines EU-RISE-Projektes entwickelt wurden.

Als Themen für Graduierungsarbeit werden

i) die Optimierung von optischen und elektrischen Schichteigenschaften
ii) die Verbesserung der Schichthaftung auf Hochleistungslegierungen und
iii) die Komplettierung eines neuen Schichtsystems angeboten.

Zur Charakterisierung der untersuchten Materialien stehen modernste in situ und ex situ Analysemethoden zur Verfügung.

1. Studium der Werkstoffwissenschaften, Physik oder Chemie mit überdurchschnittlichen Leistungen (Notendurchschnitt ≤ 2.0)
2. Interesse und Freude an experimenteller wissenschaftlicher Arbeit
3. Grundkenntnisse in Programmierung und sicherer Umgang mit Büro- und wissenschaftlicher Software
4. Fachkundige Englischsprachkenntnisse

internationale Forschungsumgebung, ortsübliche Aufwandsentschädigung