Helmholtz Information & Data Science School:

HEIBRiDS

  2. HIDA
  3. Forschen
  4. HEIBRiDS - Berlin / Potsdam

In die Tiefen des Universums blicken oder Erdbeben vorhersagen: An der Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS) hat die Beschäftigung mit Data Science einen weiten Horizont.

Der Berliner Forschungsverbund für Data Science

Eine einzigartige Forschungsumgebung zeichnet die Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS) aus: Hier wird die Erforschung der Kernmethoden, Algorithmen und Prozesse der Digitalisierung aus unterschiedlichen Blickwinkeln ermöglicht und Wissen zwischen unterschiedlichen Disziplinen transportiert.

HEIBRiDS vereint fünf Helmholtz-Zentren und vier Universitätspartner aus Berlin. Die beteiligten Helmholtz-Zentren verfügen über erstklassige Expertise in den Bereichen Molekularer Medizin, Astrophysik, Polar- und Meeresforschung, Materialwissenschaften und Geowissenschaften.

HEIBRiDS ermöglicht uns, noch weiter über den Tellerrand zu schauen. Ich versuche, mein Thema daher immer auch aus datenwissenschaftlicher Sicht zu betrachten und Methoden anzuwenden, die vielleicht noch keiner angewendet hat.

Gregor Pfalz

ist Doktorand an der HEIBRiDS. Er analysiert Daten, die aus den Sedimenten arktischer Seen stammen, um Klimavorhersagen zu treffen. Lesen Sie mehr zu seinem Projekt

Die HEIBRiDS im Portrait

Mission

In einem interdisziplinären Promotionsprogramm werden junge Wissenschaftlerinnen und Wissenschaftler in datenwissenschaftlichen Anwendungen in einem breiten Spektrum naturwissenschaftlicher Bereiche ausgebildet.

Das Ziel von HEIBRiDS ist, eine neue Generation von Data Scientists auszubilden, die die Anforderungen und Herausforderungen derjenigen Disziplinen verstehen, in denen die Datenwissenschaft unverzichtbar geworden ist.

 

Forschungsbereiche

Die Teilnehmer des HEIBRiDS-Programms promovieren in sehr unterschiedlichen Forschungsbereichen: von Erde & Umwelt bis hin zu Materie. Diese Vielfalt macht dieses Graduiertenprogramm so einzigartig.

Übersicht über aktuelle Promotionsprojekte

„Ich fand Data Science spannend und wollte etwas Neues lernen. Informatik, Datenwissenschaft und Satelliten sind eine sehr schöne Kombination.“

Olga Kondrateva, Doktorandin an der HEIBRiDS

Mehr über die Forschung an der HEIBRiDS

Curriculum

  • Betreuung: Tandembetreuung durch eine Universität und einen Helmholtz-Partner; halbjährliche Treffen mit beiden Betreuern; jährliches Treffen mit dem interdisziplinären Thesis Advisory Committee
  • Kurse zur wissenschaftlichen Weiterbildung und zur Schulung persönlicher Fähigkeiten: Individuell gestalteter Lehrplan entsprechend dem jeweiligen Forschungsprofil. Zugang für alle HEIBRiDS-Doktorandinnen und -Doktoranden zu Kursen aus einem umfangreichen Kursangebot der Berliner Universitätsallianz und der Helmholtz-Partner sowie zu speziell für die Doktorandinnen und Doktoranden des Programms organisierten Kursen
  • Obligatorische Teilnahme an den Doktorandenseminaren und den Data Science Lectures, die zweimal im Monat stattfinden
  • HEIBRiDS-Retreat: Präsentation des eigenen Forschungsprojekts und des Feedbacks der Programm-PIs auf dem jährlichen HEIBRiDS-Retreat
  • Teilnahme an (internationalen) Konferenzen

Finanzierung und Dauer des Programms

Das Programm erstreckt sich über vier Jahre und bietet eine volle Finanzierung. Die Vergütung während der Laufzeit entspricht der Tarifstufe E13 des TVöD bzw. des TV-L (abhängig von der Institution, an der die Anstellung erfolgt).

Bewerbung und weitere Informationen

HEIBRiDS-Standort ist Berlin, je nach disziplinärer Anbindung ergeben sich abweichende Standorte für die Promovenden im Berliner Umkreis. Programmsprache ist Englisch.

 

Kontakt

PD Dr. Eirini Kouskoumvekaki

Zur HEIBRiDS Website

Bewerben

Folgende Institutionen sind an HEIBRiDS beteiligt

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI)
Deutsches Elektronen-Synchrotron (DESY)
GFZ Helmholtz-Zentrum für Geoforschung
Helmholtz-Zentrum Berlin für Materialien und Energie (HZB)
Max-Delbrück-Centrum für Molekulare Medizin (MDC)
Technischer Universität Berlin
Charité-Universitätsmedizin Berlin
Freier Universität Berlin
Humboldt-Universität zu Berlin

Unsere Doktoranden

Ekin Celikkan
GFZ - KIT

Daniel Collin
GFZ - TU Berlin

Binayak Ghosh
Doktorand HEIBRiDS

Paolo Graniero
Doktorand HEIBRiDS

Brian Groenke
Doktorand HEIBRiDS

Viktoriia Huryn
MDC - Charité

Olga Kondrateva

Olga Kondrateva
Doktorandin HEIBRiDS

Daniel León Periñán
MDC - TU Berlin - Charité

Oleksii Martynchuk

Oleksii Martynchuk
Doktorand HEIBRiDS

Abhay Mehta
DESY - HU Berlin

Lusinè Nazaretyan
Doktorandin HEIBRiDS

Sergey Redyuk
Doktorand HEIBRiDS

Elizabeth Robertson

Elizabeth Robertson
Doktorandin HEIBRiDS

Jonas Schaible
HZB - FU Berlin

Hermann Stolte

Hermann Stolte
Doktorand HEIBRiDS

Christian Utama
Doktorand HEIBRiDS

Femke van Geffen

Femke van Geffen
Doktorandin HEIBRiDS

Nadja Veigel

Nadja Veigel
Doktorandin HEIBRiDS

Xiaoyan Yu

Xiaoyan Yu
Doktorandin HEIBRiDS

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Forschen an der HEIBRiDS

An der HEIBRiDS findet im Raum Berlin vielfältige Forschung im Bereich Data Science statt - von der Genetik bis hin zu Klimaforschung. Lesen Sie hierzu die School-Reportage Pioniere mit breitem Horizont: Data Scientists an der HEIBRiDS

Grünes Licht für Data Science-Nachwuchskräfte von der HEIBRiDS im Raum Berlin-Brandenburg (Foto: Patrick Robert Doyle/Unsplash)
HEIBRiDS Reportage

Mehr über die Forschung an der HEIBRiDS

Lesen Sie hier mehr über die vielfältige Forschung an der HEIBRiDS und über ihre Doktorandinnen und Doktoranden.

 

Pioniere mit breitem Horizont: Data Scientists an der HEIBRiDS

Nachwuchskräfte, die sich mit Data Science und einer Fachdisziplin auskennen, sind hochgefragt. An der Helmholtz-Graduiertenschule HEIBRiDS kooperieren elf namhafte Institutionen in Berlin-Brandenburg, um junge Forscherinnen und Forscher an dieser Schnittstelle auszubilden. Das Themenspektrum reicht dabei von der Genforschung bis hin zur Astronomie.

 

„Unser Bedarf an IT-Experten wächst stetig“

Die Digitalisierung prägt die Medizin zusehends. So profitiert die Entwicklung neuer Impfstoffe und Medikamente immer stärker von Datenwissenschaften und KI. Pfizer-Manager Peter Albiez erläutert, wie Graduiertenschulen wie die HEIBRiDS dabei helfen, den enormen Bedarf an Data-Science-Fachkräften zu decken.

 

So lernen Satelliten zu kommunizieren

Um Brände zu erkennen und Dürreperioden vorherzusagen, müssen Satelliten effizient Daten mit der Erde austauschen. HEIBRiDS-Doktorandin Olga Kondrateva programmiert die Satelliten so, dass sie selbst die Daten auswählen, deren Übertragung sinnvoll ist.

 

Zeitreisen in der Arktis

An der Berliner Data Science School HEIBRiDS analysiert Gregor Pfalz Daten aus Sedimenten arktischer Seen. So will er Vorhersagen für das Klima der Zukunft machen. Wie das geht? Mit viel Geduld und dem Vermählen zweier Disziplinen – der Geologie und der Informatik.

 

Satellit über der Erde (Foto: DLR)

PD Dr. Eirini Kouskoumvekaki

Wissenschaftliche Koordinatorin HEIBRiDS

eirini.kouskoumvekaki@mdc-berlin.de

+49-160 96586637

Max Delbrueck Center for Molecular Medicine
Building 84, Room 1015
Robert-Roessle-Str. 10
13125 Berlin

Zur School

Contact

Ekin Celikkan
Bayesian Machine Learning with Uncertainty Quantification for Detecting Weeds in Crop Lands from Low Altitude Remote Sensing (2022 - )

Supervisors:

Martin Herold (GFZ)

Nadja Klein (KIT)

 

Weeds are significant contributors (about 12% of global crop production) to crop yield and quality decline. Farmers use different approaches such as chemical or biological herbicides to eliminate weeds. However, excess use of herbicides leads to the pollution of soils, water, and air, putting the above and below ground wildlife biodiversity at risk. Alternative weed mitigation strategies must be designed and promoted. The site-specific weed management (SSWM) approach has been proposed consisting of varying weed management strategies within a crop field to suit the weed population's variation in density, location, and composition. The first step in implementing a SSWM strategy is accurate and timely detection and mapping of weeds. The high temporal, spatial, and spectral remote sensing information is required to capture detailed within-field variability, which can only be met by using the Low Altitude Remote Sensing platform and lightweight hyperspectral imaging sensors that can be deployed locally and at varying conditions.

This research aims to use hyperspectral multi-temporal Low altitude remote-based time-series imagery combined with field data and advanced machine learning (ML) techniques to detect and discriminate weeds in croplands. To achieve this aim, the project will i) monitor the weed population during different growth stages and preprocess of hyperspectral data and field data for better detecting weeds, ii) test a combination of ML and image processing algorithms for detecting weeds across a range of field and growing stage conditions, iii) apply the framework for multi-temporal analysis to track weed distribution and conditions for theuptake by precision farming techniques in our study regions.

 

Full-length publications

  1. E. Celikkan, M. Saberioon, M. Herold and N. Klein (2023). Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 582-592.
  2. E. Celikkan, T. Kunzmann, Y. Yeskaliyev, S. Itzerott, N. Klein, and M. Herold (2025). WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields. Proceedings of the Winter Conference on Applications of Computer Vision (WACV), pp. 4767-4777.

 

Conference presentations

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E-mail

Contact

Daniel Collin
Predicting geomagnetic conditions on the Earth from multi-spectral images of the Sun by combining data science and physical models (2022 - )

Supervisors:

Yuri Shprits (GFZ)

Guillermo Gallego (TU)

 

Space weather is a term used to describe hazardous events in the near-Earth space environment that can have adverse effects. Power grids, telecommunication infrastructure and space assets show significant vulnerability to space weather events originating from the Sun. While these effects are largely invisible to the naked eye, in the 21st century operations in space and on the ground significantly depend on the accurate knowledge and forecast of the conditions in the space environment.

Using ground observations of the Sun and physics-based models of the Solar System, it is possible to quantify space weather hazards (e.g., solar wind speed and density) and risks on Earth’s technology. The output of this method provides an estimate of the desired variables. These methods, however do not utilize the vast amount of observations that are available from space, and they are computationally demanding and dominantly physics-based, making them difficult to run in real-time and use all available measurements.

We propose to leverage the growing number of highly detailed multi-spectral images of the Sun to improve predictions of the solar wind streams arriving at the Earth. We propose to exploit the capabilities of modern computer vision and machine learning (ML) techniques to register solar images, analyze them and assimilate them in an empirical (data-driven) model. Through our approach we will develop a novel, data-driven framework directly connecting solar disturbances to its consequences for space weather.

 

Full-length publications

  1. D. Collin, Y. Shprits, S.J. Hofmeister, S. Bianco, and G. Gallego (2025). Forecasting High-Speed Solar Wind Streams from Solar Images. Space Weather. https://doi.org/10.1029/2024SW004125

 

Conference presentations

  1. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed from solar EUV images. (Oral and poster presentation), International Workshop on Machine Learning and Computer Vision in Heliophysics, Sofia, Bulgaria, 19-21 April 2023.
  2. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed by machine learning based on coronal hole characteristics. (Poster presentation), EGU General Assembly , Vienna, Austria, 24–28 April 2023. https://doi.org/10.5194/egusphere-egu23-6968
  3. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed from solar EUV images. (Oral presentation), IUGG General Assembly, Berlin, Germany, 11-20 July 2023. https://doi.org/10.57757/IUGG23-2070
  4. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed from coronal holes. (Oral presentation), AGU Fall Meeting, San Francisco, USA, 11–15 December 2023.
  5. D. Collin, Y. Shprits, S. Bianco, F. Inceoglu, S. Hofmeister, and G. Gallego. Forecasting solar wind speed from coronal holes and active regions. (Poster presentation), EGU General Assembly, Vienna, Austria, 14–19 April 2024. https://doi.org/10.5194/egusphere-egu24-18676
  6. D. Collin, Y. Shprits, S. Hofmeister, S. Bianco, and G. Gallego. Forecasting solar wind speed with solar images. (Oral and poster presentation), International Magnetosphere Coupling Workshop IV, Potsdam, Germany, 3–7 June 2024.
  7. D. Collin, Y. Shprits, S. Bianco, S. Hofmeister, and G. Gallego. Forecasting Solar Wind Speed from Solar EUV Images. (Poster presentation), Helmholtz AI Conference, Düsseldorf, Germany, 12-14 Jun 2024.
  8. D. Collin, Y. Shprits, S. Bianco, S. Hofmeister, and G. Gallego. Using Distributional Regression to Improve Solar Wind Speed Forecasting from Solar Images. (Oral presentation), European Space Weather Week, Coimbra, Portugal, 4-8 Nov 2024.

 

E-mail

Kontakt

Binayak Ghosh
Projekttitel: "Online Learning and Decision Making for Real-Time Analytics of Synthetic Aperture Radar (SAR) Data

Supervisors:

Mahdi Motagh (GFZ)

Setareh Maghsudi (Uni Tübingen)

Mehr Erfahren

Kontakt

Paolo Graniero
Projekttitel: "Optimization of Solar Energy Yield and Specific Load Conditions Considering Electric Busses in Public Transportation"

Supervisors:

Natalia Kliewer (FU)

Carolin Ulrbrich (HZB)

Rutger Schlatmann (HZB)

Mehr Erfahren

Kontakt

Brian Groenke
Projekttitel: "Quantifying and explaining uncertainty in modeling permafrost thaw under a warming climate"

Supervisors:

Julia Boike (AWI)

Guillermo Gallego (TU)

Mehr Erfahren

Contact

Viktoriia Huryn
Multi-resolution models for single-cell genomics data (2022 - )

Supervisors:

Uwe Ohler (MDC)

Markus Schuelke-Gerstenfeld (Charité)

 

Single-cell genomics can obtain molecular data for tens of thousands of cells simultaneously. A typical experiment is carried out on a complex sample that contains different cell types, and can measure different cellular properties, such as the number of messenger RNA molecules per cell. Typical tasks include identifying distinct cell types (e.g. via unsupervised embeddings, [1]) or inferring a pseudo-temporal ordering of cells along developmental stages. A particular opportunity arises from single-cell genome accessibility data, which provides information about which of several million gene switches, so called regulatory regions, are accessible/on or inaccessible/off [2]. These data can be analyzed at multiple resolutions: At the level of whole regions, to identify where active switch regions are and to infer which genes they may regulate, or at the level of short DNA sequence patterns within the regions, which are recognized by proteins to specifically activate the switches in e.g. different cell-types. Models to utilize the power of single-cell genomics data, and accessibility in particular, are still in their infancy. The main challenge is that the higher number of cells (i.e. samples) is accompanied by high dropout: the readout covers only a few percent of all variables, and the resulting discrete count data is sparse. Additionally, ground truth experimental data only exists for a handful of scenarios, making it hard to develop practically useful methods that work beyond simulated data.

The project will utilize data from the Schuelke lab to develop deep neural network approaches in the Ohler lab that enable flexible multi-resolution analyses: the goal is to devise models that are able to infer both active regulatory regions and the functional sequence patterns in them, while (a) leveraging data from smaller or larger cell neighborhoods as needed; (b) accounting for confounders such as variable dropout and cell type mixtures; and (c) utilizing auxiliary data from other single-cell experiments.

 

Full-length publications

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Conference presentations

  1. V. Huryn, R. Monti, A.A. Rakowski, and V. Döring. Disentanglement learning for functional genomics data. (Poster presentation), Kipoi Summit. New Horizons in Computational Regulatory Genomics. Zugspitze, Germany, 25-26 September 2023.
E-mail

Kontakt

Olga Kondrateva
Olga Kondrateva
Projekttitel: "On-board Image Classification based on Space-Based FPGA Processing"

Supervisors:

Björn Scheuermann (HU)

Winfried Halle (DLR)

mehr Erfahren

Contact

Daniel León Periñán
Towards molecular digital pathology: leveraging spatial transcriptomics and deep learning to predict gene expression from tissue morphology in solid tumors (2022 - )

Supervisors:

Nikolaus Rajewsky (MDC)

Klaus-Robert Müller (TU)

Frederich Klauschen (Charité)

 

Despite enormous progress in the understanding, early diagnosis and treatment, solid tumors still account for a quarter of deaths. Solid tumors arise from somatic cells through the accumulation of molecular alterations that eventually lead to their uncontrolled proliferation, invasion of healthy tissues and ultimately, distant metastases. In current clinical practice, the best treatment is chosen by assessing clinical presentation, histological tumor type and molecular characteristics, such as oncogenic mutations and, in some cases, gene expression profiles. The evaluation of histomorphological tumor properties in combination with molecular profiles guides risk-adjusted, personalized therapies that aim to optimize outcome for individual patients. However, diagnostic molecular profiling currently mostly relies on techniques performed on bulk tissue without providing any spatial information about the observed molecular tumor properties. While this can already make the interpretation of mutational profiles challenging, spatially resolved profiling becomes indispensable for the molecular analysis of the tumor microenvironment composed of multiple different cell types whose complex interactions influence therapy response. In this context, single-cell sequencing techniques may offer a unique opportunity to offer both high spatial and molecular resolution in the context of complex tumor histology.

Recently, the fruitful interdisciplinary collaboration between clinical, technological, and computational researchers has cultivated rapid progress in the field of digital pathology. AI-based models provide support to diagnostic pathology, for instance by automating tumor identification and tissue classification and cell detection from tumor histology images. Current computational models, however, are limited in their ability to predict tumor molecular characteristics due to the scarcity of paired imaging and molecular training data. Development of spatial transcriptomics assays aim to fill this gap by enabling unbiased, transcriptome-wide profiling of mRNA expression in intact tissue sections. Thus, they represent an ideal source of such paired morphological and molecular data with unprecedented resolution for training the next generation of digital pathology algorithms.

The project aims to push forward the field of digital pathology by predicting gene expression in tissue space from histomorphology alone. To achieve that, we will train deep learning models on the high-resolution gene expression maps provided by spatial transcriptomics co-registered with the corresponding histomorphology images. Advances in explainable AI approaches will be leveraged to reveal which morphological features and areas are exploited by such models to predict gene expression. This would be vital to allow for a transparent decision process, crucial for medical applications, and would also deepen our understanding of tumor biology by correlating tissue and cellular composition/histomorphology of the tumor and its microenvironment with function. We anticipate such a computational approach to have a positive impact on clinical practices by facilitating the prediction of molecular properties from routine diagnostic H&E images and thus to complement or even partially replace molecular testing.

 

Full-length publications

  1. T.M. Pentimalli, S. Schallenberg, D. León-Periñán, ..., F. Klauschen, and N. Rajewsky (2023). High-resolution molecular atlas of a lung tumor in 3D. bioRxiv. https://doi.org/10.1101/2023.05.10.539644
  2. M. Schott, D. León-Periñán,.., T.M. Pentimalli, …, N. Karaiskos, and N. Rajewsky (2024). Open-ST: High-resolution spatial transcriptomics in 3D. Cell. https://doi.org/10.1016/j.cell.2024.05.055

Conference presentations

  1. D. León-Periñán, N. Karaiskos, M. Schott, E. Splendiani, E. Senel, and N. Rajewsky. Computational methods for high-resolution spatial transcriptomics. (Poster presentation), VIB Spatial Omics, Ghent, Belgium, 13-14 June 2024.
E-mail

Kontakt

Oleksii Martynchuk
Oleksii Martynchuk
Projekttitel: "Identification of rock falls in Mars Reconnaissance Orbiter images using machine learning"

Supervisors:

Jürgen Oberst (DLR)

Odej Kao (TU)

 

Mehr Erfahren

Contact

Abhay Mehta
Context awareness in real-time image classification for ground-based gamma-ray telescopes (2022 - )

Supervisors:

David Berge (DESY)

Matthias Weidlich (HU)

 

The sensitivity of ground-based gamma-ray telescopes is ultimately limited by their ability to reconstruct the properties of gamma-rays from the particle shower produced when they interact with the atmosphere and reject the much more numerous background of showers from charged cosmic rays. 

At this time, array sensitivity can be considered as “software limited” and as such has continuously improved in the past 20 years by exploiting advanced image reconstruction and classification algorithms [1] and multivariate classification techniques such as boosted decision trees [2] and neural networks. It is clear that using modern machine learning techniques can improve this performance further. Yet, telescope arrays typically make observations under a huge range of context conditions. As such, it is of utmost importance to integrate contextual data on the operation of the telescope as well as on atmospheric conditions into any data analysis pipeline. This is challenging not only because of the heterogeneity of contextual data, but also from a computational point of view. 

This PhD project sets out to develop data processing pipelines that combine deep learning techniques for gamma-ray telescope data with diverse types of contextual data to dramatically improve the telescopes’ sensitivity. To this end, we will draw on initial results on employing state-of-the-art machine learning techniques to gamma-ray telescope data [3] as well as technical insights into integration of static datasets into stream processing pipelines [4].

 

Full-length publications

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Conference presentations

  1. A. Mehta. Machine Learning for Imaging Atmospheric Cherenkov Telescope (IACT) Background Rejection. H.E.S.S. Collaboration Meeting, Bordeaux, France, 24-29 September 2023.

 

E-mail

Kontakt

Lusinè Nazaretyan
Projekttitel: "Scoring pathogenicity of human genome variants"

Supervisors:

Martin Kircher (Charité)

Dieter Beule (MDC)

Ulf Leser (HU)

Mehr Erfahren

Kontakt

Sergey Redyuk
Projekttitel: "End-to-End Management of Experimental Data Science on Biomedical Molecular Data"

Supervisors:

Volker Markl (TU)

Uwe Ohler (MDC)

Mehr Erfahren

Kontakt

Elizabeth Robertson
Elizabeth Robertson
Projekttitel: "Building a Photonic Processor for Energy-Efficient AI"

Supervisors:

Janik Wolters (DLR)

Guillermo Gallego (TU)

Mehr Erfahren

Contact

Jonas Schaible
Data-driven performance optimization of coloured and textured solar modules (2022 - )

Supervisors:

Christiane Becker (HZB)

Christof Schütte (FU)

Sven Burger (ZIB)

 

With the growing share of photovoltaic (PV) solar energy in the global energy generation capacity, building-integrated photovoltaics (BIPV) gains increasing importance. For BIPV, aesthetical factors, such as color, play a larger role than for large industrial PV fields. The structural color of PV modules and surface texturing for minimizing reflective losses and for self-cleaning have to be considered for accurately estimating the optical performance and, subsequently, the annual energy yield. This raises several issues which are hardly considered in state-of-the-art numerical methods, which are used for planning PV systems and for estimating their performance: Which specific module surface treatments ensure maximum energy yield? How does the module appear visually to an observer throughout the day and year? How do the spectral albedo of the surroundings, local weather conditions and local shadowing affect the result? How can the complex environments in building-integrated and bifacial solar modules be simulated as efficiently as possible?

This project aims to develop an all-encompassing optical modeling and optimization toolbox for individual PV modules considering the full optical circumstances from specifically textured module surfaces to local solar irradiance conditions. Together with urban planners and architects, desirable color effects will be identified. Metrics will be developed in order to quantify the aesthetical effect of color appearance of BIPV modules. The PhD student will model PV modules with different textured surfaces and coloring techniques, and account for shadowing and spectral reflection of the surroundings, e.g. from overgrown ground and trees.

 

Full-length publications

  1. J. Schaible, H. Winarto, V. Škorjanc, D. Yoo, L. Zimmermann, K. Jäger, I. Sekulic, P.-I. Schneider, S. Burger, A. Wessels, B. Bläsi, and C. Becker (2024). Optimizing Aesthetic Appearance of Perovskite Solar Cells Using Color Filters. Solar RRL. https://doi.org/10.1002/solr.202400627

Conference presentations

  1. J. Schaible, B. Nouri, T. Kotzab, M. Loevenich, N. Blum, A. Hammer, K. Jäger, C. Becker and S. Wilbert. Application of Nowcasting to Reduce the Impact of Irradiance Ramps on PV Power Plants. (Oral presentation), EU PVSEC, Lisbon, Portugal, 18-22 September 2023.
  2. J. Schaible, H. Winarto, D. Yoo, L. Zimmermann, A. Wessels, K. Jäger, B. Bläsi, S. Burger, C. Becker. On aesthetical appearance of colored perovskite solar modules. (Oral presentation), SPIE Photonics Europe, Strasbourg, France, 8-12 April 2024.
  3. J. Schaible, H. Winarto, D. Yoo, L. Zimmermann, A. Wessels, K. Jäger, B. Bläsi, S. Burger, C. Becker. On aesthetical appearance of colored perovskite solar modules. (Poster presentation),16th Annual Meeting Photonic Devices (AMPD2024), Berlin, Germany, 17-19 April 2024.
E-mail

Kontakt

Hermann Stolte
Hermann Stolte
Projekttitel: "Dynamic Scheduling of Gamma-ray Source Observations"

Supervisors:

Matthias Weidlich (HU)

Elisa Pueschel (DESY)

Mehr Erfahren

Kontakt

Christian Utama
Projekttitel: "Explainable Artificial Intelligence and Trust in the Energy Sector"

Supervisors:

Natalia Kliewer (FU)

Rutger Schlatmann (HZB)

Christian Meske (Ruhr-University of Bochum)

 

Mehr Erfahren

Kontakt

Femke van Geffen
Femke van Geffen
Projekttitel: "New routines to explore modern genomic data to assess ancient DNA records from the Last Ice Age"

Supervisors:

Ulrike Herzschuh (AWI)

Begüm Demir (TU)

Mehr Erfahren

Kontakt

Nadja Veigel
Nadja Veigel
Projekttitel: "Data Mining Dynamic Human Behaviors for Flood Risk Assessment in Coupled Human-environment Systems"

Supervisors:

Andrea Cominola (TU)

Heidi Kreibich (GFZ)

Mehr Erfahren

Kontakt

Xiaoyan Yu
Xiaoyan Yu
Projekttitel: "Deep Learning with sparse annotations for the analysis of lung tissue microscopy images"

Supervisors:

Dagmar Kainmueller (MDC)

Andreas Hocke (Charité)

Marina Höhne (TU-Berlin)

Mehr Erfahren