Humboldt-Universität zu Berlin - Abteilung für Haushalt und Personal

Humboldt-Universität zu Berlin | Universitätsverwaltung | Abteilung für Haushalt und Personal | Stellenausschreibungen | Research Assistant (m/f/d) with expected full time, salary grade E 13 TV-L HU (third-party funding limited for 3 years, intended to be hired as of 01.10.2022)

Research Assistant (m/f/d) with expected full time, salary grade E 13 TV-L HU (third-party funding limited for 3 years, intended to be hired as of 01.10.2022)

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Kennziffer
SClol-C4-44
Kategorie(n)
Wissenschaftliches Personal
Anzahl der Stellen
1
Einsatzort

Faculty of Mathematics and Natural Sciences, Department of Computer Science

Bewerbung bis
25.11.21
Text

The Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Department of Computer Science, invites applications for a PhD position for the Cluster of Excellence “Science of Intelligence”.

What are the principles of intelligence, shared by all forms of intelligence, no matter whether artificial or biological, whether robot, computer program, human, or animal? And how can we apply these principles to create intelligent technology? Answering these questions - in an ethically responsible way - is the central scientific objective of the new Cluster of Excellence Science of Intelligence (https://www.scioi.de), where researchers from a large number of analytic and synthetic disciplines - artificial intelligence, machine learning, control, robotics, computer vision, behavioral biology, psychology, educational science, neuroscience, and philosophy - join forces to create a multi-disciplinary research program across universities and research institutes in Berlin. Interdisciplinary research projects have been defined (https://www.scienceofintelligence.de/research/projects), which combine analytic and synthetic research and which address key aspects of individual, social, and collective intelligence.


Working field

Project/Doctoral project: Efficient Model Learning from Data with Partially Incorrect Labels

 The doctoral project focuses on the development of sample selection methods for model learning in the presence of noisy labels. One aim in particular is to improve uncertainty estimation to reduce confirmation bias and develop strategies to prevent semantic drift in the sample selection process. In a second step these strategies will be combined with semi-supervised learning approaches to also make use of instances that were discarded as incorrectly labeled in the sample selection step. Finally, the semi-supervised sample selection methods will be validated in settings that more closely resemble real-world applications, e.g., streaming- and online learning.


Duties:

  • Conducting experimental research in machine learning
  • Design approaches for machine learning in the face of noisy labels in data
  • Interaction within the SCIoI cluster of excellence
  • Compilation of the results for presentations, project reports, and publications

Responsibilities include scientific research within the project and academic services in the Cluster. PhD position includes the enrollment in the Cluster's doctoral program. All positions require participation in research colloquia, lecture series and workshops, as well as an active engagement in the Cluster's research activities.


Requirements:

Applicants must - when the project begins in October 2022 - hold a diploma or masters degree in computer science, or a related field with a strong focus on deep learning. The ideal candidate has a strong background in machine learning (deep learning / natural language processing).

 

The successful applicant should have

  • excellent mathematical skills
  • in depth programming skills in Python
  • very good programming skills in deep learning frameworks such as PyTorch
  • very good command of English (spoken and written)
  • strong interest in machine learning and a keen interest in understanding intelligence
  • the strong communicative skills required for interdisciplinary research
  • a conscientious work approach, flexibility, good time management, and ability to work in a team

Please include a link to your GitHub profile in the application!


Application procedure:

Candidates should upload their application preferably via the portal http://jobs.scienceofintelligence.de in order to receive full consideration.

Applications should include: motivation letter, curriculum vitae, transcripts of records (for both BSc and MSc), copies of degree certificates (BSc, MSc), proof of English skills, abstracts of Bachelor- and Master-thesis, list of publications (if applicable), two names of qualified persons who are willing to provide references, and any documents candidates feel may help us assess their competence.

Please send your application quoting the reference number to Technische Universität Berlin - The President – Cluster SClol, Sekr. SClol, Marchstr. 23, 10587 Berlin.


HU is seeking to increase the proportion of women in research and teaching, and specifically encourages qualified female scholars to apply. Researchers from abroad are welcome to apply. Severely disabled applicants with equivalent qualifications will be given preferential consideration. People with an immigration background are specifically encouraged to apply.


Please visit our website www.hu-berlin.de/stellenangebote, which gives you access to the legally binding German version.