Stellenangebote Promotionen

Doctoral Researcher in the Field of ‚Cognitive Psychology‘ (m/f/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

The Department of Liberal Arts and Sciences is currently offering an opening for a fully funded doctoral research opportunity (100% position – TVL E13) supervised by Prof. Dr. Magdalena Abel (Cognitive Psychology Lab).

We are seeking highly motivated and talented individuals to join our dynamic and international research team. You will contribute to cutting-edge research within the field of Cognitive Psychology and especially human learning and memory. Potential topics include remembering in individuals, small social groups, and large collectives. You will also contribute to teaching in the field of Cognitive Psychology and assist with administrative tasks.

The earliest possible starting date is March 1, 2025; a later start is possible.

Your Main Tasks

• Research and teaching at UTN
• Presentation and publication of your research results at conferences and in journals
• Collaboration with other researchers

Your Profile

• An outstanding Master’s degree in Psychology, Cognitive Science, or a related field
• Experience in experimental research, and a strong interest in cognitive psychology
• Solid methodological and statistical skills
• Excellent proficiency in both written and spoken English
• Programming expertise is a plus (but not a must)

Interested?

To apply for admission to doctoral research, please send your application (Code: LIAS-CPL-1) to stars@utn.de. Applications will be reviewed on a rolling basis. To receive full consideration, please apply until January 7, 2025. Applications received after that date might still be considered.

Please include the following documents:

• A personal statement explaining why you want to pursue a doctorate in this area at UTN (limited to one page)
• A complete, chronical, tabular curriculum vitae (CV) in English
• Certificates of your university degrees (M.Sc. degree and B.Sc.) or other equivalent qualification. Candidates may already apply if they expect to obtain their M.Sc. degree soon.
• Transcript of records, diploma supplements, or overview of courses from your university degrees (M.Sc. degree and B.Sc.)

All application documents should be submitted as one single PDF file.

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

If you are shortlisted, you will be invited for an interview.

For any content-related inquiries, please contact Prof. Dr. Magdalena Abel (magdalena.abel@utn.de). For general questions, please reach out to stars@utn.de.

Doctoral Researcher in the Field of Bilevel, Robust, and Discrete Optimization (m/f/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

The Department of Liberal Arts and Sciences is currently offering an opening for a fully funded doctoral research opportunity (100% position – TVL E13) supervised by Prof. Dr. Johannes Thürauf (Professor for Discrete Optimization).

The successful applicant will work in the area of bilevel and/or robust optimization. The focus of the research will be the development of models and optimization techniques, e.g., in the context of network optimization such as resilient energy and utility networks. In particular, the development of algorithms and solution techniques that yield proven optimal solutions to optimization problems will be part of the research. This involves techniques like linear, nonlinear, and integer programming, robust optimization, bilevel optimization, graph algorithms, and polyhedral combinatorics. The developed algorithms will be implemented in a programming language such as Python or C++.

The earliest possible starting date is 15.1.2025 a later start is possible.

Your Main Tasks

• Research and teaching at the UTN
• Publication of your research results at conferences and journals
• Collaboration with other researchers

Your Profile:

• An outstanding master’s degree in Mathematics, Operations Research, Theoretical Computer Science or a related field.
• A strong mathematical background in optimization, preferable knowledge in robust and/or bilevel optimization.
• Profound programming skills, e.g., in Python, Java, or C++. Experience with optimization software such as Cplex, Gurobi, or SCIP is highly desirable.
• Strong proficiency in both written and spoken English is essential.

Interested?
To apply for admission to doctoral research, please send your application (Code: LiAS-DOPT-24-01) to stars@utn.de. Applications will be reviewed on a rolling basis. To receive full consideration, please apply until 24.11.2024. Applications received after that date might still be considered. Your application should include:

• A personal statement that explains why you want to pursue a doctorate at UTN as well as why this research area and department interests you (no longer than 1 page).
• A complete, chronological, tabular curriculum vitae (CV) in English.
• Certificates of your university degrees (M.Sc. degree and B.Sc.) or other equivalent qualification. Candidates may already apply if they expect to obtain their M.Sc. degree soon.
• Transcript of records, diploma supplements, or overview of courses from your university degrees (M.Sc. degree and B.Sc.).
• Title and short abstract of your master’s thesis.

All application documents should be submitted as one single PDF file.

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview.

Please direct all inquiries regarding scientific content to Johannes Thürauf (johannes.thuerauf@utn.de). For general questions, contact stars@utn.de.

Doctoral Researcher in the Field of ‚Applied Discrete Mathematics‘ (m/f/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

The Department of Liberal Arts and Sciences is currently offering an opening for a fully funded doctoral research opportunity (100% position – TVL E13) supervised by Prof. Dr. Christoph Hertrich (Professor for Applied Discrete Mathematics).

We are seeking highly motivated and talented individuals to join our dynamic and international research team and contribute to cutting-edge research within the intersection of discrete mathematics, theoretical computer science, optimization, and machine learning. You will contribute to teaching in the field of Applied Discrete Mathematics and assist with administrative task.

The earliest possible starting date is 1st of January 2025; a later start is possible.

Your Profile:

• An excellent M.Sc. degree in Mathematics or Theoretical Computer Science.
• Preferably knowledge in one or several of the following fields: polyhedral geometry, combinatorial optimization, algorithms and complexity, theory of neural networks.
• Basic coding abilities are required; advanced coding experience, e.g., in optimization or machine learning is an advantage.
• High motivation to pursue curiosity- and application-driven mathematical research with practical impact. Initial research experience is an advantage.
• Ability and willingness to communicate and collaborate in interdisciplinary and international teams.
• Ability and willingness to contribute to teaching.
• Proficiency in oral and written English.

Interested?
To apply for admission to doctoral research, please send your application (Code: LiAS-ADM-24-01) to stars@utn.de. Applications will be reviewed on a rolling basis. To receive full consideration, please apply until 17.11.2024. Applications received after that date might still be considered. Your application should include:

• A personal statement that explains why you want to pursue a doctorate at UTN as well as why this research area and department interests you.
• A complete, chronological, tabular curriculum vitae (CV) in English.
• Certificates of your university degrees (M.Sc. degree and B.Sc.) or other equivalent qualification. Candidates may already apply if they expect to obtain their M.Sc. degree soon.
• Transcript of records, diploma supplements, or overview of courses from your university degrees (M.Sc. degree and B.Sc.).

All application documents should be submitted as one single PDF file.

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview.

Please direct all inquiries regarding scientific content to Christoph Hertrich (christoph.hertrich@posteo.de). For general questions, contact stars@utn.de.

Doctoral Researcher in the Field of ‚Natural Language Processing / Science‘ (m/f/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

At UTN, we are at the forefront of NLP, AI, and computer vision, developing innovative algorithms and techniques to tackle the key challenges in this field. We collaborate in interdisciplinary teams, constantly pushing the boundaries of NLP and AI.

The Natural Language Learning & Generation (NLLG) Lab (Prof. Dr. Steffen Eger) at the Department of Engineering of the University of Technology Nuremberg (UTN) is currently offering an opening for a fully funded doctoral research opportunity (100% position – TVL E13) on the topic:

NLP for Science

We are seeking a highly motivated and talented individual to join our dynamic and international research team and contribute to cutting-edge research in the field of NLP for science. The focus will be on developing NLP approaches that foster (semi-)automated scientific production. This includes creating LLM models capable of generating scientific figures or tables from textual descriptions (e.g., AutomatikZ or DeTikZify), writing parts of scientific papers, describing/captioning figures or tables, or even conducting scientific experiments and describing them in paper format.

Your Main Tasks:

• Research and teaching at the Department of Engineering of UTN
• Collaboration with other researchers
• Publication of your research results at top-quality conferences and journals

Your Profile:

• An outstanding Master’s degree in Natural Language Processing, Computational Linguistics, Computer Science, Computer Vision, Artificial Intelligence, or a related field
• A strong background and genuine interest in NLP, machine learning, or computer vision and interdisciplinary research
• Proficiency in Python and familiarity with machine learning frameworks like PyTorch or TensorFlow and handling of LLMs
• Strong mathematical, problem-solving, and analytical skills
• Effective communication and presentation abilities in English

Interested?
To apply for admission to doctoral research, please send your application by 04.11.2024 (Code: ENG-NLLG-24-05) to stars@utn.de. Your application should include:

• A personal statement explaining why you want to pursue a doctorate in this area at UTN (limited to one page)
• A complete, chronological, tabular curriculum vitae (CV)
• Certificates of your university degrees (M.Sc. and B.Sc.) or equivalent qualification
• Transcripts of records, diploma supplements, or an overview of courses from your degrees (M.Sc. and B.Sc.)
• Your M.Sc. thesis
• (Optional) A link to your GitHub projects and any prior publications
• All application documents should be submitted in ONE SINGLE PDF

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview, a research presentation, and a coding or analytical exercise.

Please direct all inquiries regarding scientific content to Steffen Eger (steffen.eger@utn.de). For general questions, contact stars@utn.de.

Doctoral Researcher in the Field of ‚Natural Language Processing / Evaluation‘ (m/f/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

At UTN, we are at the forefront of NLP, AI, and computer vision, developing innovative algorithms and techniques to tackle the key challenges in this field. We collaborate in interdisciplinary teams, constantly pushing the boundaries of NLP and AI.

The Natural Language Learning & Generation (NLLG) Lab (Prof. Dr. Steffen Eger) at the Department of Engineering of the University of Technology Nuremberg (UTN) is currently offering an opening for a fully funded doctoral research opportunity (100% position – TVL E13) on the topic:

Multimodal Multi-Agent Evaluation of Generative AI

We are seeking a highly motivated and talented individual to join our dynamic and international research team and contribute to cutting-edge research in the field of multimodal multi-agent NLP evaluation. The focus will be on developing robust, efficient, and high-quality evaluation metrics for text generation/generative AI. These metrics may extend existing methods like BERTScore, BARTScore, or GEMBA to multimodal settings, such as text-to-image generation, or incorporate multi-agent approaches like multi-agent debate for evaluation.

Your Main Tasks:

• Research and teaching at the Department of Engineering of UTN
• Collaboration with other researchers
• Publication of your research results at top-quality conferences and journals

Your Profile:

• An outstanding Master’s degree in Natural Language Processing, Computational Linguistics, Computer Science, Computer Vision, Artificial Intelligence, or a related field
• A strong background and genuine interest in NLP, machine learning, or computer vision and interdisciplinary research
• Proficiency in Python and familiarity with machine learning frameworks like PyTorch or TensorFlow and handling of LLMs
• Strong mathematical, problem-solving, and analytical skills
• Effective communication and presentation abilities in English

Interested?
To apply for admission to doctoral research, please send your application until 02.11.2024 (Code: ENG-NLLG-24-06) to stars@utn.de. Your application should include:

• A personal statement explaining why you want to pursue a doctorate in this area at UTN (limited to one page)
• A complete, chronological, tabular curriculum vitae (CV)
• Certificates of your university degrees (M.Sc. and B.Sc.) or equivalent qualification
• Transcripts of records, diploma supplements, or an overview of courses from your degrees (M.Sc. and B.Sc.)
• Your M.Sc. thesis
• (Optional) A link to your GitHub projects and any prior publications
• All application documents should be submitted in ONE SINGLE PDF

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview, a research presentation, and a coding or analytical exercise.

Please direct all inquiries regarding scientific content to Steffen Eger (steffen.eger@utn.de). For general questions, contact stars@utn.de.

Doctoral Researcher in the Field of ‚Natural Language Processing / Large Language Models‘ (m/f/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

At UTN, we are at the forefront of NLP, AI, and computer vision, developing innovative algorithms and techniques to tackle the key challenges in this field. We collaborate in interdisciplinary teams, constantly pushing the boundaries of NLP and AI.

The Natural Language Learning & Generation (NLLG) Lab (Prof. Dr. Steffen Eger) at the Department of Engineering of the University of Technology Nuremberg (UTN) is currently offering an opening for a fully funded doctoral research opportunity (100% position – TVL E13) on the topic:

Next Generation Large Language Models

We are seeking a highly motivated and talented individual to join our dynamic and international research team and contribute to cutting-edge research in the field of Large Language Models. The focus will be on developing next-generation LLMs, which could include highly efficient smaller models, multimodal or multilingual LLMs, or models with enhanced reasoning abilities and reduced limitations like biases or hallucinations.

Your Main Tasks:

• Research and teaching at the Department of Engineering of UTN
• Collaboration with other researchers
• Publication of your research results at top-quality conferences and journals

Your Profile:

• An outstanding Master’s degree in Natural Language Processing, Computational Linguistics, Computer Science, Computer Vision, Artificial Intelligence, or a related field
• A strong background and genuine interest in NLP, machine learning, or computer vision and interdisciplinary research
• Proficiency in Python and familiarity with machine learning frameworks like PyTorch or TensorFlow and handling of LLMs
• Strong mathematical, problem-solving, and analytical skills
• Effective communication and presentation abilities in English

Interested?
To apply for admission to doctoral research, please send your application until 31.10.2024 (Code: ENG-NLLG-24-04) to stars@utn.de. Your application should include:

• A personal statement explaining why you want to pursue a doctorate in this area at UTN (limited to one page)
• A complete, chronological, tabular curriculum vitae (CV)
• Certificates of your university degrees (M.Sc. and B.Sc.) or equivalent qualification
• Transcripts of records, diploma supplements, or an overview of courses from your degrees (M.Sc. and B.Sc.)
• Your M.Sc. thesis
• (Optional) A link to your GitHub projects and any prior publications
• All application documents should be submitted in ONE SINGLE PDF

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview, a research presentation, and a coding or analytical exercise.

Please direct all inquiries regarding scientific content to Steffen Eger (steffen.eger@utn.de). For general questions, contact stars@utn.de.

„Promotion an der UTN im Bereich „AI and Robotics – Multi-Modal Generative Models with Interpretability for Robust Robotic Manipulation” (m/w/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.
At UTN, we are at the forefront of robotics, working on developing innovative algorithms and techniques to tackle the key challenges in this exciting field. We collaborate in interdisciplinary teams, exploring the possibilities of integrating robotics into various application domains. Joining our team means becoming part of a progressive and dynamic research environment where we constantly strive to push the boundaries of robotics at UTN.

The Artificial Intelligence and Robotics Lab (Prof. Dr. Wolfram Burgard) and the Machine Intelligence Lab (Prof. Dr. Florian Walter) at the Department of Engineering of the University of Technology Nuremberg (UTN) are currently offering openings for fully funded doctoral research opportunities (100% position – TVL E13) on the topic:

Multi-Modal Generative Models with Interpretability for Robust Robotic Manipulation

We are seeking highly motivated and talented individuals to join our dynamic and international research team and contribute to cutting-edge research in the field of artificial intelligence and robotics. The focus will be on intelligent robot manipulation based on tactile sensing and state-of-the-art foundation models, such as Stable Diffusion.

Your Main Tasks:

• Research and teaching at the Department of Engineering of UTN
• Collaboration with other researchers
• Publication of your research results at conferences and in journals

Your Profile:

• An outstanding Master’s degree in Computer Science, Electrical Engineering, Artificial Intelligence, or Robotics.
• A strong background and genuine interest in robotics, machine learning, or computer vision.
• Proficiency in Python and familiarity with machine learning frameworks like PyTorch or TensorFlow would be advantageous.
• Exceptional mathematical, problem-solving, and analytical skills.
• Effective communication and presentation abilities in English are also crucial in this role.

Interested?
To apply for admission to doctoral research, please send your application (Code: ENG-GENIUS-24-02) to stars@utn.de. Your application should include:

• A personal statement that explains why you want to pursue a doctorate in this area at UTN
• A complete, chronical, tabular curriculum vitae (CV)
• Certificates of your university degrees (M.Sc. degree and B.Sc.) or other equivalent qualification
• Transcripts of records, diploma supplements, or overview of courses from your university degrees (M.Sc. degree and B.Sc.)
• Your M.Sc. thesis
• (Optional) A link to your GitHub projects and any prior publications

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview and an opportunity to present your research work.

Please direct all inquiries regarding scientific content to Florian Walter (florian.walter@utn.de). For general questions, please stars@utn.de.

„Promotion an der UTN im Bereich „AI and Robotics – Foundation Models for Robotics” (m/w/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.
At UTN, we are at the forefront of robotics, working on developing innovative algorithms and techniques to tackle the key challenges in this exciting field. We collaborate in interdisciplinary teams, exploring the possibilities of integrating robotics into various application domains. Joining our team means becoming part of a progressive and dynamic research environment where we constantly strive to push the boundaries of robotics at UTN.

The Artificial Intelligence and Robotics Lab (Prof. Dr. Wolfram Burgard) and the Machine Intelligence Lab (Prof. Dr. Florian Walter) at the Department of Engineering of the University of Technology Nuremberg (UTN) are currently offering openings for fully funded doctoral research opportunities (100% position – TVL E13) on the topic:

Foundation Models for Robotics

We are seeking highly motivated and talented individuals to join our dynamic and international research team and contribute to cutting-edge research in the field of artificial intelligence and robotics. The focus will be on the design, training, and application of foundation models for robotics applications, such as perception, control, navigation and manipulation.

Your Main Tasks:

• Research and teaching at the Department of Engineering of UTN
• Collaboration with other researchers
• Publication of your research results at conferences and in journals

Your Profile:

• An outstanding Master’s degree in Computer Science, Electrical Engineering, Artificial Intelligence, or Robotics.
• A strong background and genuine interest in robotics, machine learning, or computer vision.
• Proficiency in Python and familiarity with machine learning frameworks like PyTorch or TensorFlow would be advantageous.
• Exceptional mathematical, problem-solving, and analytical skills.
• Effective communication and presentation abilities in English are also crucial in this role.

Interested?
To apply for admission to doctoral research, please send your application (Code: ENG-RIG-24-03) to stars@utn.de. Your application should include:

• A personal statement that explains why you want to pursue a doctorate in this area at UTN
• A complete, chronical, tabular curriculum vitae (CV)
• Certificates of your university degrees (M.Sc. degree and B.Sc.) or other equivalent qualification
• Transcripts of records, diploma supplements, or overview of courses from your university degrees (M.Sc. degree and B.Sc.)
• Your M.Sc. thesis
• (Optional) A link to your GitHub projects and any prior publications

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview and an opportunity to present your research work.

Please direct all inquiries regarding scientific content to Florian Walter (florian.walter@utn.de). For general questions, please stars@utn.de.

Promotion an der UTN im Bereich „Energy Systems & Market Design“ (m/w/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

The Department of Liberal Arts and Sciences with its Energy Systems and Market Design Lab is currently offering several openings for doctoral research opportunities (3 years, 75%, TVL-13). We are seeking highly motivated and talented individuals to join our dynamic and international research team, which has built up a strong expertise in the area of energy market modelling and is involved in various joint activities with industrial partners and policy consulting projects. The Lab participates in the collaborative research center DFG Transregio 154: Mathematical Modelling, Simulation and Optimization using the Example of Gas Networks and the Kopernikus project ARIADNE on policymaking for the German energy transition. The research group offers a lively research environment, financial support for attending conferences, and an intensive supervision within a large and interactive team.

Your Tasks:

The applicant will work at the Energy Systems and Market Design Lab, which brings together interdisciplinary methodological and applied research on the energy transformation. Within a specific research project, the candidate will focus on one of the following topics:

(i) German and European energy market design
(ii) regional incentives for flexibility in electricity markets
(iii) the energy transition in urban quarters
(iv) sector coupling and energy prices
(v) international hydrogen markets

The successful candidate will contribute the perspectives of their research to the activities of the lab and benefit from the team’s diverse perspectives on the transformation of the economy and society. A particular joint focus of the research group is the interplay between the energy transformation and artificial intelligence. The successful candidate is expected to work towards obtaining a Ph.D. within the doctoral program at UTN.

Your Profile:

Applicants should have a qualified degree in industrial engineering, economics, mathematics, operations research, or a closely related discipline with fundamental knowledge in microeconomics and mathematical modelling. The ideal candidate has initial experience in theoretical or empirical research in energy markets and should have a strong quantitative background. Proficiency in oral and written German and English is required.

If your profile matches the requirements, please send the following documents:

• A personal statement that explains why you want to pursue a doctorate at UTN as well as which research area and department interests you and why
• A complete, chronical, tabular curriculum vitae (CV) in English
• Certificates of your university degrees (M.Sc. degree and B.Sc.) or other equivalent qualification
• Transcript of records, diploma supplements, or overview of courses from your university degrees (M.Sc. degree and B.Sc.)

Interested?

To apply for admission to doctoral research, please send your application to (Code: LiAS-ESMD-24-01) stars@utn.de.

For more information on the application process and admission requirements see https://www.utn.de/en/research/doctoral-degree/.

If you are shortlisted, you will be invited for an interview and an opportunity to present your research work.
For any content-related inquiries, please contact Dr. Jonas Egerer (jonas.egerer@utn.de). For general questions, please reach out to stars@utn.de.

Promotion an der UTN im Bereich „User Experience, Learning Experience Design, und Usability Research“ (m/w/d)

Die Technische Universität Nürnberg (UTN) bietet eine inspirierende und interdisziplinäre Forschungsumgebung mit Zugang zu modernsten Ressourcen. Sie ist der ideale Ort, um zukunftsträchtige Entdeckungen zu machen und einen bedeutenden Beitrag zu spannenden Forschungsfeldern zu leisten.

Die Technischen Universität Nürnberg (UTN) bietet die Möglichkeit einer voll finanzierten interdisziplinären Promotion. Wir suchen hoch motivierte und talentierte Personen, die unser dynamisches und internationales Forschungsteam verstärken und einen Beitrag im Bereich User Experience (UX) and Learning Experience (LX) Research (inkl. Usability, soziotechnische Heuristiken) leisten. Sie werden unter anderem im Projekt des Campusmanagementsystems der UTN mitarbeiten, z.B. zur Demonstration zur User Experience in UTNexus, und Sie sind in agile Rückkopplungsprozesse mit dem internen Softwareentwicklungsteam eingebunden und arbeiten ggfs. mit einer externen Dienstleistung zusammen. Ein Campus-Management-System (CaMS) kann als eine sozio-technische Verflechtung von technischen Artefakten und sozialen Praktiken verstanden werden. Aus dieser Sicht ist weitere Forschung im Bereich UX und LX erforderlich.

Ihre Aufgabe:

• Im Projekt des Campusmanagementsystems der UTN untersuchen Sie, wie dieses System optimiert werden kann hinsichtlich Benutzerfreundlichkeit, „look & feel“ Effektivität und Effizienz sowie Orientierung an Standards, z.B. ISO 9241
• Untersuchung eines Mehrwerts von sozio-technischen Heuristiken (socio-technical-pedagogical Usability) gegenüber bekannten Methoden wie beispielsweise System Usability Scale, Optimierung der Benutzererfahrung, Anwenden und Weiterentwicklung von User-Centered-Design Strategien
• Forschung zu soziotechnischen Heuristiken und Weiterentwicklung von user experience oder learning experierence Methoden
• Aufbau des UX-LX-Labs inkl. Anleitung studentischer Mitarbeitenden

Ihr Profil:

• Abgeschlossenes wissenschaftliches Hochschulstudium (Staatsexamen, Diplom oder Master) mit Bezug zu User Experience oder Usability (Gebrauchstauglichkeit)
• Interesse an interdisziplinärer Kooperation in Forschung
• Wünschenswert sind erste Erfahrungen im UX Bereich
• Gute Deutsch- und Englischkenntnisse oder Bereitschaft, Deutsch/Englisch zu erlernen

Interessiert?

Senden Sie bitte Ihre Bewerbung auf Zulassung zur Promotion an stars@utn.de. Weitere Informationen zu den Zulassungsvoraussetzungen finden Sie unter https://www.utn.de/de/forschung/promotion/.

Wenn Sie in die engere Auswahl kommen, erhalten Sie eine Einladung zu einem Interview und haben die Möglichkeit, Ihre bisherige Forschungsarbeit vorzustellen.

Bitte richten Sie alle inhaltlichen Anfragen an Gründungsvizepräsidentin Prof. Dr. Isa Jahnke (vp-learning@utn.de). Bei allgemeinen Fragen wenden Sie sich bitte an stars@utn.de.

Promotion an der UTN im Bereich „Klassische Philologie mit dem Schwerpunkt
Gräzistik zu einem Thema der Platonforschung“ (m/w/d)

Die Technische Universität Nürnberg (UTN) bietet eine inspirierende und interdisziplinäre Forschungsumgebung mit Zugang zu modernsten Ressourcen. Sie ist der ideale Ort, um zukunftsträchtige Entdeckungen zu machen und einen bedeutenden Beitrag zu spannenden Forschungsfeldern zu leisten.

Das Department Liberal Arts und Sciences an der Technischen Universität Nürnberg (UTN) bietet die Möglichkeit einer voll finanzierten interdisziplinären Promotion. Wir suchen hoch motivierte und talentierte Personen, die unser dynamisches und internationales Forschungsteam verstärken und einen Beitrag zur Spitzenforschung im Bereich Klassische Philologie mit dem Schwerpunkt Gräzistik zu einem Thema der Platonforschung leisten.

Das Department of Liberal Arts and Sciences der Technischen Universität Nürnberg befindet sich im Aufbau. Es strebt eine besonders intensive Kooperation zwischen Geistes-, Sozial- und Naturwissenschaften sowie mit den Ingenieurwissenschaften an. Die Technische Universität Nürnberg verfolgt das Ziel, Wissen und Knowhow geisteswissenschaftlicher Disziplinen in die Gestaltung des Wandels hin zu einer nachhaltigen Gesellschaft einzubringen. Es wird erwartet, dass Promovierende bereit sind, sich in diese Forschungskooperationen des Departments einzubringen. Zusätzlich übernehmen Promovierende pro Semester mindestens eine Lehrveranstaltung in den verschiedenen, gerade in der Konzeption befindlichen Studiengängen.

Ihr Profil:

• Abgeschlossenes wiss. Hochschulstudium (Staatsexamen oder Master) in Klassischer Philologie
• Prädikatsexamen in Klassischer Philologie (Hauptfach Gräzistik); nachgewiesene vertiefte Kenntnisse in der antiken Philosophie, bes. Platonismus und Aristotelismus der Antike und Spätantike
• Breite literaturwissenschaftliche Kenntnisse der zentralen Autoren der antiken griechischen Literatur- und Wissensgeschichte
• Interesse an interdisziplinärer Kooperation in Forschung und Lehre

Im Rahmen der wissenschaftlichen Qualifizierung erwartet Sie die Mitarbeit in Forschung und Lehre bei Frau Prof. Dr. Gyburg Uhlmann. Die Forschungsschwerpunkten liegen dabei bei „Artes liberales: Wissenschaft und Bildung“ und „Rhetorik und Philosophie: Meinung und begründetes Wissen“ sowie allgemein zur Philosophie, Bildungstheorie und -praxis, Rhetorik und Wissenschaftstheorie im  4. Jh. v. Chr. in Athen sowie den verschiedenen Formen des Platonismus und Aristotelismus der Antike und Spätantike. Zu den Aufgaben gehört weiterhin die Unterstützung bei der Organisation wissenschaftlicher Veranstaltungen und die Koordination von Schulkooperationsveranstaltungen. Die Dissertation soll zu einem Thema der Platonforschung im Bereich oben genannten Forschungsschwerpunkte angefertigt werden.

Interessiert?

Senden Sie bitte Ihre Bewerbung auf Zulassung zur Promotion an stars@utn.de. Weitere Informationen zu den Zulassungsvoraussetzungen finden Sie unter https://www.utn.de/de/forschung/promotion/.

Wenn Sie in die engere Auswahl kommen, erhalten Sie eine Einladung zu einem Interview und haben die Möglichkeit, Ihre bisherige Forschungsarbeit vorzustellen.

Bitte richten Sie alle inhaltlichen Anfragen an Frau Prof. Dr. Uhlmann (gyburg.uhlmann@utn.de). Bei allgemeinen Fragen wenden Sie sich bitte an stars@utn.de.

Opportunity for doctoral research at UTN (m/w/d)

The University of Technology Nuremberg (UTN) offers a stimulating and interdisciplinary research environment with access to cutting-edge resources. It is the ideal place to make groundbreaking discoveries and contribute to exciting fields of research.

The Department of Engineering at UTN is currently offering openings for two fully funded doctoral research opportunities (100% position – TVL E13) in the Machine Learning lab headed by Prof. Dr. Josif Grabocka.

https://www.utn.de/departments/department-engineering/machine-learning-lab/

We are seeking highly motivated and talented individuals to join our dynamic and international research team and contribute to cutting-edge research in machine learning. You will also assist Prof. Dr. Josif Grabocka in terms of teaching support and other administrational tasks.

Position Requirements:

• A M.Sc. degree in Mathematics or Computer Science with top grades
• Advanced knowledge of Math, Probability, Statistics and Linear Algebra
• A M.Sc. thesis with a deep focus on Machine Learning, which goes beyond simply applying ML to a specific application
• Very good knowledge of PyTorch and training large-scale Deep Learning models

How do I know if my level of Machine Learning knowledge is sufficient?
If you know well and comfortably understand most chapters of the following book, then your ML level is sufficient. (https://github.com/probml/pml-book/releases/latest/download/book1.pdf)

If your profile matches the requirements, please send the following documents as a zip file “name.surname.zip” with the subject „Application – Ph.D.“.

• a personal statement that explains why you want to pursue a doctorate at UTN as well as which research area and department interests you and why
• a copy of your identity card or passport
• a complete, chronical, tabular curriculum vitae (CV) in English
• certificates of your university degrees (M.Sc. degree and B.Sc.) or other equivalent qualification
• transcript of records, diploma supplements, or overview of courses from your university degrees (M.Sc. degree and B.Sc.)
• your M.Sc. thesis
• A link to your GitHub projects
• [Optional] Any prior publications

To apply for admission to doctoral research, please send your application to stars@utn.de.

For more information on the application process and admission requirements see
https://www.utn.de/en/research/doctoral-degree/.
For more information on the TV-L E13 payscale see https://oeffentlicher-dienst.info/tv-l/allg/.

If you are shortlisted, you will be invited for an interview and an opportunity to present your research work.

For any inquiries, please contact: machine-learning@utn.de.