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Research Assistant (PhD candidate/PostDoc) - Risk-sensitive choice and reinforcement learning under uncertainty

Technische Universität Berlin offers an open position:

Research Assistant - salary grade E 13 TV-L (PhD candidate/PostDoc)

Risk-sensitive choice and reinforcement learning under uncertainty

Duration: 3 years

Starting Date: April, 1st, 2019

Application deadline: January, 18th, 2019

Faculty IV - Institute of Software Engineering and Theoretical Computer Science / Neural Information Processing

Working field:

The successful candidate is supposed to join a DFG funded project (with Co-PI Dirk Ostwald, Freie Universität Berlin) which combines computational modelling with behavioral and fMRI experiments. The goal is to better understand human decision making and reward-based learning under perceptual uncertainty. The computational framework generalizes the risk-sensitive MDP approach of Yun et al. 2014 (Neural Comput. 26, 1298ff) to a POMDP-based behavioral modelling framework. Planned experiments address response time behavior and neural reinforcement learning processes under perceptual risk. The work program can be biased either towards computational or experimental work depending on the qualifications of the candidate.

Qualifications:

Successfully completed university degree in a subject relevant to the work program; very good programming and English language skills; solid background in decision making and reward-based learning on a theoretical and / or experimental level; experience with computational modelling and the model-based analysis of behavioral and fMRI data is desirable

How to apply:

Please send your application with the usual documents (CV, letter of motivation, transcripts of records, certificates, and the names of two persons who can provide recommendation letters) to Prof. Dr. Klaus Obermayer, FG Neuronale Informationsverarbeitung, Sekr. MAR 5-6, Marchstr. 23, 10587 Berlin or by e-mail to

To guarantee equal opportunities between men and women, applications of women with respective qualifications are explicitly welcome. Severely handicapped persons will be privileged on equal qualification.

All applications received before January 18th will receive full considerations.

Please send copies only. Original documents will not be returned.

 

 

Research Assistant (PhD candidate) - Efficient Multi-Task Deep Learning

Technische Universität Berlin offers an open position:

Research Assistant - salary grade E 13 TV-L (PhD candidate)

Efficient Multi-task Deep learning

Duration: 3 years

Starting Date: July, 1st, 2019

Application deadline: February, 1st, 2019

Faculty IV - Institute of Software Engineering and Theoretical Computer Science / Neural Information Processing

Working field:

The successful candidate will carry out experiments for training deep learning models and will analyze the generated representations for a battery of visual tasks. Various transfer learning paradigms will be implemented and analyzed in order to shed light on the following questions:

  • Can one find representations that are optimal for multiple but related objectives?
  • Does deep learning even construct representations where multi-optimal solutions outperform specialized representations thus mitigating the need for large datasets before adding new tasks
  • Can the presence of multiple tasks be used to isolate elements of the representation, assign roles to them, and identify the conditions under which the network makes decisions?

Deep representations will be compared with visual representations in the human brain recorded during object recognition tasks. Efficient transfer learning techniques will be evaluated in the context of a synthetic system for rapid object recognition and visual search.

The position is part of the new DFG-funded cross-disciplinary research Cluster “Science of Intelligence” (http://www.scienceofintelligence.de/). The successful candidate will be enrolled in the Cluster's doctoral program and is expected to actively engage in the Cluster's educational and research activities.

Qualifications:

Applicants must hold a Master degree in Computational Neuroscience, Computer Science, Physics, Mathematics, or related fields. Applicants should have very good programming skills, a very good command of the English language, strong competence in machine learning, and a strong interest in working at the interface of machine learning and cognitive science. Practical experience with deep learning techniques is a plus.

How to apply:

Please upload your application via the link www.scienceofintelligence.de/call-for-applications/open-positions/doctoral-project-efficient-multi-task-deep-learning/) and follow instructions.

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

To guarantee equal opportunities between men and women, applications of women with respective qualifications are explicitly welcome. Severely handicapped persons will be privileged on equal qualification.

 

 

Research Assistant (PhD candidate) - Computational Models of Task Dependent Object-based Visual Attention

Technische Universität Berlin offers an open position:

Research Assistant - salary grade E 13 TV-L (PhD candidate)

Computational Models of Task Dependent Object-based Visual Attention

Duration: 3 years

Starting date: September 1st, 2019 (earlier starting date may be possible)

Application deadline: February 15th, 2019

Faculty IV - Institute of Software Engineering and Theoretical Computer Science / Neural Information Processing

Working field:

The successful candidate will explore the hypothesis that object-level attentional units are essential mid-level factors which guide human eye-movements in visual scene analysis. Based on eye-fixation data from visual search tasks she/he will first build computational models to emulate the measured fixation sequences, to quantify the influence of different low- and high-level visual features, and to characterize the influence of task-driven changes in object-based attention processes. In a second step, plausible models will be integrated as “attentional modules” into a computer vision system for visual scene analysis and will be evaluated in terms of task success and the number of computations involved. Potential achievement of the project is an efficient real-time analysis of dynamic visual scenes.

The position is part of the new DFG-funded cross-disciplinary research Cluster “Science of Intelligence” (http://www.scienceofintelligence.de/). The successful candidate will be enrolled in the Cluster's doctoral program and is expected to actively engage in the Cluster's educational and research activities.

Qualifications:

Applicants must hold a Master degree in Computational Neuroscience, Computer Science, Physics, Mathematics, or related fields. Applicants should have very good programming skills, a very good command of the English language, a solid mathematical background, competence in machine learning, and a strong interest in visual perception.

How to apply:

Please upload your application via the link www.scienceofintelligence.de/call-for-applications/open-positions/doctoral-project-computational-models-of-task-dependent-object-based-visual-attention/) and follow instructions.

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

To guarantee equal opportunities between men and women, applications of women with respective qualifications are explicitly welcome. Severely handicapped persons will be privileged on equal qualification.

 

 

Student Research Assistant (40h/month) - Dynamics and control of inter-areal brain networks

Mitarbeit im Teilprojekt B8: Dynamics and control of inter-areal brain networks" des SFB 910 "Control of self-organizing nonlinear systems".

Ausschreibungskennziffer: SFB 910-B8-SH

Einstellungsdauer: voraussichtlich vom 01.01.2019 - 31.12.2020

Arbeitsgebiet:

Die Aufgaben umfassen die mathematische Beschreibung, Implementierung und Durchführung von numerischen Experimenten mit neuronalen Netzwerkmodellen (Populationsmodelle) für Untersuchungen (1) zu den von realistischen Netzwerkgraphen unterstützen dynamische Zuständen und (2) ihrer Kontrolle durch den Einfluss externer elektrischer Felder.

Erwünschte Kenntnisse und Fähigkeiten:

Gute Programmierer- und Englischkenntnisse sind erforderlich. Kenntnisse über Differentialgleichungen / Dynamische Systeme und Stochastische Prozesse sind von großem Vorteil. Basiswissen in Neurobiologie ist
wünschenswert.

Bewerbung:

Ihre schriftliche Bewerbung mit Lebenslauf, Immatrikulationsbescheinigung und ggf. aktueller Notenübersicht richten Sie bitte an Prof. Dr. Klaus Obermayer, FG Neuronale Informationsverarbeitung, Sekr. MAR 5-6, Marchstr. 23, 10587 Berlin oder per e-mail an
Zur Wahrung der Chancengleichheit zwischen Männern und Frauen sind Bewerbungen von Frauen mit der jeweiligen Qualifikation ausdrücklich erwünscht. Schwerbehinderte werden bei gleicher Eignung bevorzugt.

 

 

Student Reseach Assistant (40h/Month) - Efficient Multi-task Deep Learning

Technische Universität Berlin offers an open position:

Student Reseach Assistant (40h/Month)

Efficient Multi-task Deep Learning

Duration: 3 years

Starting date: July, 1st, 2019

Application deadline: February 15th, 2019

Faculty IV - Institute of Software Engineering and Theoretical Computer Science / Neural Information Processing

Working field:

The successful candidate will carry out experiments for training deep learning models and will analyze the generated representations for a battery of visual tasks. Various transfer learning paradigms will be implemented. Deep representations will be compared with visual representations in the human brain recorded during object recognition tasks. Efficient transfer learning techniques will be evaluated in the context of a synthetic system for rapid object recognition and visual search.

The position is part of the new DFG-funded cross-disciplinary research Cluster “Science of Intelligence” (http://www.scienceofintelligence.de/) and the successful candidate is welcome to participate in all of the Cluster's research and educational activities.

Qualifications:

Applicants should have very good programming skills, a good command of the English language, competence in machine learning, and a strong interest in working at the interface of machine learning and cognitive science. Some practical experience with deep learning techniques is a plus.

How to apply:

Please send your application with the usual documents (CV, letter of motivation, transcripts of records, and certificates) to Prof. Dr. Klaus Obermayer, Sekr. MAR 5-6, Marchstr. 23, 10587 Berlin, or (preferably) by e-mail to

To guarantee equal opportunities between men and women, applications of women with respective qualifications are explicitly welcome. Severely handicapped persons will be privileged on equal qualification.

Please send copies only. Original documents will not be returned.

 

 

Please contact Prof. Dr. Klaus Obermayer for information about other possible job openings

Prof. Dr. Klaus Obermayer
Neural Information Processing Group
MAR 5-6, Marchstrasse 23
10587 Berlin, Germany
email: oby(at)ni.tu-berlin.de

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