© Universität Bielefeld
Marie Skłodowska-Curie Actions
Innovative Training Networks (ITN) / Doctoral Networks as Coordinating Institution
ALPACA
Algorithms for analyzing millions of genomes simultaneously
While all different, genomes from the same species agree in the vast majority of places. This asks some intriguing questions to computer scientists: what data structures and algorithms can we employ to store and analyze millions of genomes from one species in a resource- and real-world-application friendly manner? The EU International Training Network ALPACA, coordinated by Alexander Schönhuth, deals with these problems, and trains a new generation of computer scientists who are able to think and work in terms of the new structures and algorithms developed.
EPOC
Wie können Gesellschaft und Politik mit Herausforderungen umgehen, die durch hohe Unsicherheit, große Komplexität und dynamische Veränderung gekennzeichnet sind? Beispiele dafür gibt es viele, etwa den Klimawandel oder die Entwicklung und Verbreitung technischer Innovationen. Das Innovative Training Network EPOC (“Economic Policies in Complex Environments“) treibt die Entwicklung und Anwendung moderner Computer-gestützter Modelle und Analysemethoden voran, die geeignet sind, auch in komplexen und herausfordernden Umfeldern Effekte von Politikmaßnahmen fundiert zu analysieren und Entscheidungsunterstützung zu liefern. EPOC verfolgt dieses Ziel durch die Kombination einer interdisziplinären Forschungsagenda mit einem innovativen Promotionsprogramm, das gemeinsam von mehreren europäischen Universitäten entwickelt und durchgeführt wird.
Magicbullet::Reloaded
Tumorzellen gezielt bekämpfen
Trotz ständiger Weiterentwicklung neuer und effizienterer Behandlungsmethoden bleibt Krebs weltweit die zweithäufigste Todesursache. Paul Ehrlich, Nobelpreisträger für Physiologie von 1908, hatte bereits früh die Vision eines zielgerichteten Medikaments, einer sogenannten „Zauberkugel“, welches nur die Tumorzellen angreift und gesunde Zellen verschont. Das von dem Chemiker Prof. Dr. Norbert Sewald koordinierte European Training Network „Magicbullet::Reloaded“ setzt Ehrlichs visionäres Konzept fort und entwickelt Konjugate aus Wirkstoffen mit Peptiden oder kleinen Molekülen, die in der Lage sind, Tumorzellen gezielt zu bekämpfen.
Innovative Training Networks (ITN) / Doctoral Networks as Partner
- ASTRA
Titel: Applying Sustainability Transition Research in Social Work tackling Major Societal Challenge of Social Inclusion
Projektleitung:
Prof. Dr. Holger Ziegler, Fakultät für Erziehungswissenschaften
Zusammenfassung:
Transdisciplinary sustainability transition research, policies and practices in social work will be combined to create a new scientific domain. By establishing new transformative standards of social work doctoral training in Europe, the EU-funded ASTRA project will pave the way for a radical new approach to tackling major societal challenges. Specifically, the project will train early-stage researchers to focus on the challenge of social inclusion of young people in precarious situations and migrants in vulnerable communities. Led by a consortium of leading European social work academics and research organisations for environmental and economic sustainability, ASTRA will investigate methodic models of nature-based well-being, environmental justice, and circular and solidarity economy as well as sustainable food policies in vulnerable communities.
Laufzeit: 10.2020-09.2024
- ESSGN
Titel: European Social Science Genetics Network
Projektleitung:
Prof. Dr. Martin Diewald
Fakultät für Soziologie
Zusammenfassung:
The European Social Science Genetics Network (ESSGN) brings together seven academic beneficiaries with a shared interest in social science genetics, i.e. in incorporating genetic information to improve our understanding of age-old questions in the social sciences, such as the origins of inequality, the ‘nature versus nurture’ debate, and the extent to which the interplay between environments and genes is important in shaping individuals’ lives. The consortium consists of an interdisciplinary group of academics, as well as seven non-academic partners committed to using data science to address inequalities in life chances. There is an urgent need for training in social science genetics due to recent technological advances in genetics, the intricacies of using genetic data, and the growing availability of such data in surveys traditionally studied by social scientists. Our aim is to train the next generation of social scientists in the responsible and technically correct use of genetic data and in objective communication about what can and cannot be learned from working with genetic data in the social sciences.
The project will go beyond the state-of-the-art (i) by using Europe’s most comprehensive multigenerational databases to separate direct genetic effects from parental genetic and socio-economic factors that shape the rearing environment; and (ii) by exploiting the large toolbox of causal inference methods used in econometrics and statistics to estimate the extent to which environments causally protect individuals with genetic disadvantages. We will (1) analyse to what extent genetic (‘nature’) and environmental (‘nurture’) factors contribute to equality of opportunity and intergenerational mobility, and (2) establish how nature and nurture jointly shape inequalities in life chances. As such, our programme of research provides novel and exciting opportunities to social scientists to deepen our understanding of how inequalities in life chances are shaped.Laufzeit: 03.2023-02.2027
- ImAge-D
Titel: Imaging Ageing Endothelium at the nanoscale - Doctorates
Projektleitung:
Prof. Dr. Thomas Huser
Fakultät für Physik
Zusammenfassung:
ImAge-D will train a new generation of Doctoral Candidates (DCs) in the development and application of newly developed high speed and high-resolution imaging tools in biomedical research. The ten DCs will be cross-pollinated with concepts and skills in physics and biomedicine, in particular in super-resolution imaging, analytical image reconstruction, and optical micro-manipulation methods. These skills will be applied to reveal for the first time the functionality and morphology (below the diffraction limit of light) of living endothelial cells (EC) that present the main barrier between the blood/lymph and all organs and tissues, and how these vital cells change with ageing. Very little is known at the nanoscale about extremely important physiological functions of EC and their role in the transfer and/or clearance of metabolites and pharmaceuticals to vital organs, and how EC change with ageing. The current generation of optical nanoscopes, however, is rather slow and can only be applied to isolated, typically fixed (i.e. dead) cells rather than biomedically relevant tissues. Also, newcomers to the field need to familiarize themselves with a whole new set of potential problems that might arise in the use of optical nanoscopy, such as image reconstruction- related artifacts to name just one example.This is an area of research where European enterprises are very active. Excellent training in new scientific and complementary skills, combined with international and intersectoral work experience, will instill an innovative, creative and entrepreneurial mind-set in ImAge-D's DCs, maximising economic benefits based on scientific discoveries. These specialised, highly trained, and mobile DCs will have greatly enhanced career prospects. The training in novel physical methods with highly relevant experience in the biomedical sciences will allow them to confidently navigate at the interface of academic, clinical and private sector research.
Laufzeit: 10.2023-09.2027
- LEMUR
Titel: Learning with Multiple Representations
Projektleitung:
Prof. Dr. Barbara Hammer
CITEC
Zusammenfassung:
Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g. as a classification problem), including a specification of the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to create a novel branch of machine learning we call Learning with Multiple Representations. We aim to develop the theoretical foundations and a first set of algorithms for this new paradigma. Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. We regard LEMUR as very timely, as LMR algorithms will allow to flexible representations (e.g. suitable for explainability, fairness) with diverse target functions (e.g. incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. We will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e. their data hunger, by means of weaker sources of labelling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competences relevant to successful careers in central AI areas.
Laufzeit: 01.2023-12.2026
- MolCal
Titel: Molecule-based magneto/electro/mechano-Calorics
Projektleitung:
Prof. Dr. Jürgen Schnack
Fakultät für Physik
Zusammenfassung:
MolCal will contribute to establishing a critical mass of researchers in promising exploratory topics on caloric materials and energy conversion technologies for solid-state cooling and heating applications at near-ambient and very-low temperatures. Temperature control systems are responsible for approximately half of the EU energy consumption expenditure. This figure alone amply justifies the need to dedicate great efforts to the search for alternative refrigeration and heat pump methods. Research on caloric materials has never been as active as it is now, due to the prospect of new-generation refrigerators and heat pumps that are energy efficient and environmentally friendly, on the one hand, and the policies on low-energy consumption and global warming refrigerants, on the other. MolCal presents an approach never tried before in similar collaborative research training programmes. We will consider caloric materials that fall under the umbrella of molecule-based materials and can respond to different sources of the driving stimulus, be it magnetic, electric, and/or mechanical. Since there is no clear-cut consensus on which type of caloric material holds the most promise, this multi-front approach will be an advantage because it will permit transfer of methods already developed from the magnetocaloric subfield into the others, which are increasingly in the spotlight because of their enormous potentiality. Furthermore, MolCal will develop devices based on low-cost barocaloric materials and, due to the molecular characteristics, will progress towards challenging applications by exploring the limits of the smallest size of magnetic refrigerators. Academic and non-academic leaders, from top research institutions in Europe and outside, will expose the doctoral researchers to integrative, multidisciplinary, and multisectoral training in chemistry, materials science, physics, device development, and relevant transversal skills.
Laufzeit: 01.2024-12.2027
- PERSEO
Titel: European Training Network on PErsonalized Robotics as SErvice Oriented applications
Projektleitung:
Prof. Dr. Friederike Eyssel
CITEC
Zusammenfassung:
The personal robotics domain is raising new challenges concerning the need for robot behaviour with a high level of personalisation with respect to each user’s needs and preferences. The aim is to have a cloud repository of robot behaviours that allows an easy personalised configuration approach. This requires the investigation of different robot capabilities to adequately understand and model the human–robot interaction and adapt the robot’s behaviour to the context. The EU-funded PERSEO project will train early-stage researchers (ESRs) from the fields of computer science, philosophy, and psychology in how robotics technology can be personalised on the physical, cognitive and social levels. This will help the ESRs understand how to address social, legal and ethical issues that arise with the uptake of personal robots.
Laufzeit:01.2021-12.2024
Research and Innovation Staff Exchange (RISE) als Partner
- PANGAIA
Titel: Pan-genome Graph Algorithms and Data Integration
Projektleitung:
Prof. Dr. Jens Stoye
CeBiTec
Zusammenfassung:
Modern sequencing technology produces genome sequence data on a gigantic scale reaching into exabytes. The emerging urgent question is how these volumes of data could be arranged and analysed in a computationally efficient and biomedically meaningful manner. This EU-funded project is going to explore graph-based representation of large genome datasets and determine their advantages over traditional sequence-based presentation of pan-genomic data. Genomes that are evolutionarily close vary only a little and graph-based pan-genomic representation allows to remove redundancies while highlighting important differences. The research is going to demonstrate the advantage of the shift to the new data representation approach using comparative analysis, compression, integration and exploitation of genome data as the fundamental point
Laufzeit: 01.2020-06.2025
Postdoctoral Fellowships
UCoCo
Stacking single-layer materials on top of the other enable the creation of materials with unique electrical and optical properties. The interlayer coupling – the interaction between the neighbouring atomic layers – is the key to the rich properties of the stacked layered materials. Recent studies have shown that the interlayer coupling can be modulated with an out-of-plane electric field on the atomic layers. Funded by the Marie Skłodowska-Curie Actions programme, the UCoCo project aims to investigate mechanisms that control the interlayer coupling in the ultrafast time scale using an out-of-plane terahertz electric field. The proposed approach could facilitate studies on the ultrafast control of quantum phases in various layered materials. It could also be applied as optoelectronic and all-optical ultrafast switches.
MONOWAX
Wachsester nachhaltig produzieren.
Wachsester (WE) sind neutrale Lipide von großer industrieller Bedeutung, die als Bestandteile von Schmiermitteln, Arzneimitteln und Kosmetika verwendet werden. Ihre Herstellung basiert auf chemischen Prozessen, bei denen hauptsächlich aus Erdöl gewonnene Rohstoffe eingesetzt werden und gefährliche Abfälle entstehen. Angesichts der schwindenden fossilen Reserven, der Nachfrage nach Industrierohstoffen und der prognostizierten Auswirkungen der globalen Erwärmung werden alternative biobasierte Plattformen für die Herstellung von WEs benötigt.
Das MONOWAX-Projekt zielt darauf ab, die alternative Produktion von WEs unter Verwendung der vielversprechenden ölhaltigen Mikroalge Monoraphidium neglectum zu erforschen. Im Rahmen des Projekts wird auch ein Vektor-Toolkit für die effiziente Genexpression in M. neglectum entwickelt. Die Ergebnisse dieses Projekts werden einen wertvollen Beitrag zur künftigen Entwicklung neuer umweltfreundlicher Strategien zur Herstellung von WEs leisten.